Synapses API Reference

This page provides API reference documentation for the Synapses module, which contains tools for creating, tuning and optimizing synaptic connections in NEURON models.

Synapse Tuning

bmtool.synapses.SynapseTuner

Source code in bmtool/synapses.py

class SynapseTuner:
    def __init__(self, mechanisms_dir: str, templates_dir: str, conn_type_settings: dict, connection: str,
                 general_settings: dict, json_folder_path: str = None, current_name: str = 'i',
                 other_vars_to_record: list = None, slider_vars: list = None) -> None:
        """
        Initialize the SynapseModule class with connection type settings, mechanisms, and template directories.

        Parameters:
        -----------
        mechanisms_dir : str
            Directory path containing the compiled mod files needed for NEURON mechanisms.
        templates_dir : str
            Directory path containing cell template files (.hoc or .py) loaded into NEURON.
        conn_type_settings : dict
            A dictionary containing connection-specific settings, such as synaptic properties and details.
        connection : str
            Name of the connection type to be used from the conn_type_settings dictionary.
        general_settings : dict
            General settings dictionary including parameters like simulation time step, duration, and temperature.
        json_folder_path : str, optional
            Path to folder containing JSON files with additional synaptic properties to update settings.
        current_name : str, optional
            Name of the synaptic current variable to be recorded (default is 'i').
        other_vars_to_record : list, optional
            List of additional synaptic variables to record during the simulation (e.g., 'Pr', 'Use').
        slider_vars : list, optional
            List of synaptic variables you would like sliders set up for the STP sliders method by default will use all parameters in spec_syn_param.

        """
        neuron.load_mechanisms(mechanisms_dir)
        h.load_file(templates_dir)
        self.conn_type_settings = conn_type_settings
        if json_folder_path:
            print(f"updating settings from json path {json_folder_path}")
            self._update_spec_syn_param(json_folder_path)
        self.general_settings = general_settings
        self.conn = self.conn_type_settings[connection]
        self.synaptic_props = self.conn['spec_syn_param']
        self.vclamp = general_settings['vclamp']
        self.current_name = current_name
        self.other_vars_to_record = other_vars_to_record
        self.ispk = None

        if slider_vars:
            # Start by filtering based on keys in slider_vars
            self.slider_vars = {key: value for key, value in self.synaptic_props.items() if key in slider_vars}
            # Iterate over slider_vars and check for missing keys in self.synaptic_props
            for key in slider_vars:
                # If the key is missing from synaptic_props, get the value using getattr
                if key not in self.synaptic_props:
                    try:
                        # Get the alternative value from getattr dynamically
                        self._set_up_cell()
                        self._set_up_synapse()
                        value = getattr(self.syn,key)
                        #print(value)
                        self.slider_vars[key] = value
                    except AttributeError as e:
                        print(f"Error accessing '{key}' in syn {self.syn}: {e}")

        else:
            self.slider_vars = self.synaptic_props


        h.tstop = general_settings['tstart'] + general_settings['tdur']
        h.dt = general_settings['dt']  # Time step (resolution) of the simulation in ms
        h.steps_per_ms = 1 / h.dt
        h.celsius = general_settings['celsius']

        # get some stuff set up we need for both SingleEvent and Interactive Tuner
        self._set_up_cell()
        self._set_up_synapse()

        self.nstim = h.NetStim()
        self.nstim2 = h.NetStim()

        self.vcl = h.VClamp(self.cell.soma[0](0.5))

        self.nc = h.NetCon(self.nstim, self.syn, self.general_settings['threshold'], self.general_settings['delay'], self.general_settings['weight'])
        self.nc2 = h.NetCon(self.nstim2, self.syn, self.general_settings['threshold'], self.general_settings['delay'], self.general_settings['weight'])

        self._set_up_recorders()

    def _update_spec_syn_param(self, json_folder_path):
        """
        Update specific synaptic parameters using JSON files located in the specified folder.

        Parameters:
        -----------
        json_folder_path : str
            Path to folder containing JSON files, where each JSON file corresponds to a connection type.
        """
        for conn_type, settings in self.conn_type_settings.items():
            json_file_path = os.path.join(json_folder_path, f"{conn_type}.json")
            if os.path.exists(json_file_path):
                with open(json_file_path, 'r') as json_file:
                    json_data = json.load(json_file)
                    settings['spec_syn_param'].update(json_data)
            else:
                print(f"JSON file for {conn_type} not found.")


    def _set_up_cell(self):
        """
        Set up the neuron cell based on the specified connection settings.
        """
        self.cell = getattr(h, self.conn['spec_settings']['post_cell'])()


    def _set_up_synapse(self):
        """
        Set up the synapse on the target cell according to the synaptic parameters in `conn_type_settings`.

        Notes:
        ------
        - `_set_up_cell()` should be called before setting up the synapse.
        - Synapse location, type, and properties are specified within `spec_syn_param` and `spec_settings`.
        """
        self.syn = getattr(h, self.conn['spec_settings']['level_of_detail'])(list(self.cell.all)[self.conn['spec_settings']['sec_id']](self.conn['spec_settings']['sec_x']))
        for key, value in self.conn['spec_syn_param'].items():
            if isinstance(value, (int, float)):  # Only create sliders for numeric values
                if hasattr(self.syn, key):
                    setattr(self.syn, key, value)
                else:
                    print(f"Warning: {key} cannot be assigned as it does not exist in the synapse. Check your mod file or spec_syn_param.")


    def _set_up_recorders(self):
        """
        Set up recording vectors to capture simulation data.

        The method sets up recorders for:
        - Synaptic current specified by `current_name`
        - Other specified synaptic variables (`other_vars_to_record`)
        - Time, soma voltage, and voltage clamp current for all simulations.
        """
        self.rec_vectors = {}
        for var in self.other_vars_to_record:
            self.rec_vectors[var] = h.Vector()
            ref_attr = f'_ref_{var}'
            if hasattr(self.syn, ref_attr):
                self.rec_vectors[var].record(getattr(self.syn, ref_attr))
            else:
                print(f"Warning: {ref_attr} not found in the syn object. Use vars() to inspect available attributes.")

        # Record synaptic current
        self.rec_vectors[self.current_name] = h.Vector()
        ref_attr = f'_ref_{self.current_name}'
        if hasattr(self.syn, ref_attr):
            self.rec_vectors[self.current_name].record(getattr(self.syn, ref_attr))
        else:
            print("Warning: Synaptic current recorder not set up correctly.")

        # Record time, synaptic events, soma voltage, and voltage clamp current
        self.t = h.Vector()
        self.tspk = h.Vector()
        self.soma_v = h.Vector()
        self.ivcl = h.Vector()

        self.t.record(h._ref_t)
        self.nc.record(self.tspk)
        self.nc2.record(self.tspk)
        self.soma_v.record(self.cell.soma[0](0.5)._ref_v)
        self.ivcl.record(self.vcl._ref_i)


    def SingleEvent(self,plot_and_print=True):
        """
        Simulate a single synaptic event by delivering an input stimulus to the synapse.

        The method sets up the neuron cell, synapse, stimulus, and voltage clamp, 
        and then runs the NEURON simulation for a single event. The single synaptic event will occur at general_settings['tstart']
        Will display graphs and synaptic properies works best with a jupyter notebook
        """
        self.ispk = None

        # user slider values if the sliders are set up
        if hasattr(self, 'dynamic_sliders'):
            syn_props = {var: slider.value for var, slider in self.dynamic_sliders.items()}
            self._set_syn_prop(**syn_props)

        # sets values based off optimizer 
        if hasattr(self,'using_optimizer'):
            for name, value in zip(self.param_names, self.params):
                setattr(self.syn, name, value)

        # Set up the stimulus
        self.nstim.start = self.general_settings['tstart']
        self.nstim.noise = 0
        self.nstim2.start = h.tstop
        self.nstim2.noise = 0

        # Set up voltage clamp
        vcldur = [[0, 0, 0], [self.general_settings['tstart'], h.tstop, 1e9]]
        for i in range(3):
            self.vcl.amp[i] = self.conn['spec_settings']['vclamp_amp']
            self.vcl.dur[i] = vcldur[1][i]

        # Run simulation
        h.tstop = self.general_settings['tstart'] + self.general_settings['tdur']
        self.nstim.interval = self.general_settings['tdur']
        self.nstim.number = 1
        self.nstim2.start = h.tstop
        h.run()

        current = np.array(self.rec_vectors[self.current_name])
        syn_props = self._get_syn_prop(rise_interval=self.general_settings['rise_interval'],dt=h.dt) 
        current = (current - syn_props['baseline']) * 1000  # Convert to pA
        current_integral = np.trapz(current, dx=h.dt)  # pA·ms

        if plot_and_print:
            self._plot_model([self.general_settings['tstart'] - 5, self.general_settings['tstart'] + self.general_settings['tdur']])    
            for prop in syn_props.items():
                print(prop)
            print(f'Current Integral in pA*ms: {current_integral:.2f}')

        self.rise_time = syn_props['rise_time']
        self.decay_time = syn_props['decay_time']


    def _find_first(self, x):
        """
        Find the index of the first non-zero element in a given array.

        Parameters:
        -----------
        x : np.array
            The input array to search.

        Returns:
        --------
        int
            Index of the first non-zero element, or None if none exist.
        """
        x = np.asarray(x)
        idx = np.nonzero(x)[0]
        return idx[0] if idx.size else None


    def _get_syn_prop(self, rise_interval=(0.2, 0.8), dt=h.dt, short=False):
        """
        Calculate synaptic properties such as peak amplitude, latency, rise time, decay time, and half-width.

        Parameters:
        -----------
        rise_interval : tuple of floats, optional
            Fractional rise time interval to calculate (default is (0.2, 0.8)).
        dt : float, optional
            Time step of the simulation (default is NEURON's `h.dt`).
        short : bool, optional
            If True, only return baseline and sign without calculating full properties.

        Returns:
        --------
        dict
            A dictionary containing the synaptic properties: baseline, sign, peak amplitude, latency, rise time, 
            decay time, and half-width.
        """
        if self.vclamp:
            isyn = self.ivcl
        else:
            isyn = self.rec_vectors[self.current_name]
        isyn = np.asarray(isyn)
        tspk = np.asarray(self.tspk)
        if tspk.size:
            tspk = tspk[0]

        ispk = int(np.floor(tspk / dt))
        baseline = isyn[ispk]
        isyn = isyn[ispk:] - baseline
        # print(np.abs(isyn))
        # print(np.argmax(np.abs(isyn)))
        # print(isyn[np.argmax(np.abs(isyn))])
        # print(np.sign(isyn[np.argmax(np.abs(isyn))]))  
        sign = np.sign(isyn[np.argmax(np.abs(isyn))])  
        if short:
            return {'baseline': baseline, 'sign': sign}
        isyn *= sign
        # print(isyn)
        # peak amplitude
        ipk, _ = find_peaks(isyn)
        ipk = ipk[0]
        peak = isyn[ipk]
        # latency
        istart = self._find_first(np.diff(isyn[:ipk + 1]) > 0)
        latency = dt * (istart + 1)
        # rise time
        rt1 = self._find_first(isyn[istart:ipk + 1] > rise_interval[0] * peak)
        rt2 = self._find_first(isyn[istart:ipk + 1] > rise_interval[1] * peak)
        rise_time = (rt2 - rt1) * dt
        # decay time
        iend = self._find_first(np.diff(isyn[ipk:]) > 0)
        iend = isyn.size - 1 if iend is None else iend + ipk
        decay_len = iend - ipk + 1
        popt, _ = curve_fit(lambda t, a, tau: a * np.exp(-t / tau), dt * np.arange(decay_len),
                            isyn[ipk:iend + 1], p0=(peak, dt * decay_len / 2))
        decay_time = popt[1]
        # half-width
        hw1 = self._find_first(isyn[istart:ipk + 1] > 0.5 * peak)
        hw2 = self._find_first(isyn[ipk:] < 0.5 * peak)
        hw2 = isyn.size if hw2 is None else hw2 + ipk
        half_width = dt * (hw2 - hw1)
        output = {'baseline': baseline, 'sign': sign, 'latency': latency,
            'amp': peak, 'rise_time': rise_time, 'decay_time': decay_time, 'half_width': half_width}
        return output


    def _plot_model(self, xlim):
        """
        Plots the results of the simulation, including synaptic current, soma voltage,
        and any additional recorded variables.

        Parameters:
        -----------
        xlim : tuple
            A tuple specifying the limits of the x-axis for the plot (start_time, end_time).

        Notes:
        ------
        - The function determines how many plots to generate based on the number of variables recorded.
        - Synaptic current and either voltage clamp or soma voltage will always be plotted.
        - If other variables are provided in `other_vars_to_record`, they are also plotted.
        - The function adjusts the plot layout and removes any extra subplots that are not needed.
        """
        # Determine the number of plots to generate (at least 2: current and voltage)
        num_vars_to_plot = 2 + (len(self.other_vars_to_record) if self.other_vars_to_record else 0)

        # Set up figure based on number of plots (2x2 grid max)
        num_rows = (num_vars_to_plot + 1) // 2  # This ensures we have enough rows
        fig, axs = plt.subplots(num_rows, 2, figsize=(12, 7))
        axs = axs.ravel()

        # Plot synaptic current (always included)
        current = self.rec_vectors[self.current_name]
        syn_prop = self._get_syn_prop(short=True,dt=h.dt)
        current = (current - syn_prop['baseline']) 
        current = current * 1000

        axs[0].plot(self.t, current)
        if self.ispk !=None:
            for num in range(len(self.ispk)):
                axs[0].text(self.t[self.ispk[num]],current[self.ispk[num]],f"{str(num+1)}")

        axs[0].set_ylabel('Synaptic Current (pA)')

        # Plot voltage clamp or soma voltage (always included)
        ispk = int(np.round(self.tspk[0] / h.dt))
        if self.vclamp:
            baseline = self.ivcl[ispk]
            ivcl_plt = np.array(self.ivcl) - baseline
            ivcl_plt[:ispk] = 0
            axs[1].plot(self.t, 1000 * ivcl_plt)
            axs[1].set_ylabel('VClamp Current (pA)')
        else:
            soma_v_plt = np.array(self.soma_v)
            soma_v_plt[:ispk] = soma_v_plt[ispk]

            axs[1].plot(self.t, soma_v_plt)
            axs[1].set_ylabel('Soma Voltage (mV)')

        # Plot any other variables from other_vars_to_record, if provided
        if self.other_vars_to_record:
            for i, var in enumerate(self.other_vars_to_record, start=2):
                if var in self.rec_vectors:
                    axs[i].plot(self.t, self.rec_vectors[var])
                    axs[i].set_ylabel(f'{var.capitalize()}')

        # Adjust the layout
        for i, ax in enumerate(axs[:num_vars_to_plot]):
            ax.set_xlim(*xlim)
            if i >= num_vars_to_plot - 2:  # Add x-label to the last row
                ax.set_xlabel('Time (ms)')

        # Remove extra subplots if less than 4 plots are present
        if num_vars_to_plot < len(axs):
            for j in range(num_vars_to_plot, len(axs)):
                fig.delaxes(axs[j])

        #plt.tight_layout()
        plt.show()


    def _set_drive_train(self,freq=50., delay=250.):
        """
        Configures trains of 12 action potentials at a specified frequency and delay period
        between pulses 8 and 9.

        Parameters:
        -----------
        freq : float, optional
            Frequency of the pulse train in Hz. Default is 50 Hz.
        delay : float, optional
            Delay period in milliseconds between the 8th and 9th pulses. Default is 250 ms.

        Returns:
        --------
        tstop : float
            The time at which the last pulse stops.

        Notes:
        ------
        - This function is based on experiments from the Allen Database.
        """
        # lets also set the train drive and delay here
        self.train_freq = freq
        self.train_delay = delay

        n_init_pulse = 8
        n_ending_pulse = 4
        self.nstim.start = self.general_settings['tstart']
        self.nstim.interval = 1000 / freq
        self.nstim2.interval = 1000 / freq
        self.nstim.number = n_init_pulse
        self.nstim2.number = n_ending_pulse
        self.nstim2.start = self.nstim.start + (n_init_pulse - 1) * self.nstim.interval + delay
        tstop = self.nstim2.start + n_ending_pulse * self.nstim2.interval
        return tstop


    def _response_amplitude(self):
        """
        Calculates the amplitude of synaptic responses for each pulse in a train.

        Returns:
        --------
        amp : list
            A list containing the peak amplitudes for each pulse in the recorded synaptic current.

        Notes:
        ------
        This method:
        1. Extracts and normalizes the synaptic current
        2. Identifies spike times and segments the current accordingly
        3. Calculates the peak response amplitude for each segment
        4. Records the indices of peak amplitudes for visualization

        The amplitude values are returned in the original current units (before pA conversion).
        """
        isyn = np.array(self.rec_vectors[self.current_name].to_python())
        tspk = np.append(np.asarray(self.tspk), h.tstop)
        syn_prop = self._get_syn_prop(short=True,dt=h.dt)
        # print("syn_prp[sign] = " + str(syn_prop['sign']))
        isyn = (isyn - syn_prop['baseline']) 
        isyn *= syn_prop['sign']
        ispk = np.floor((tspk + self.general_settings['delay']) / h.dt).astype(int)

        try:        
            amp = [isyn[ispk[i]:ispk[i + 1]].max() for i in range(ispk.size - 1)]
            # indexs of where the max of the synaptic current is at. This is then plotted     
            self.ispk = [np.argmax(isyn[ispk[i]:ispk[i + 1]]) + ispk[i] for i in range(ispk.size - 1)]
        # Sometimes the sim can cutoff at the peak of synaptic activity. So we just reduce the range by 1 and ingore that point
        except:
            amp = [isyn[ispk[i]:ispk[i + 1]].max() for i in range(ispk.size - 2)]  
            self.ispk = [np.argmax(isyn[ispk[i]:ispk[i + 1]]) + ispk[i] for i in range(ispk.size - 2)]

        return amp


    def _find_max_amp(self, amp):
        """
        Determines the maximum amplitude from the response data and returns the max in pA

        Parameters:
        -----------
        amp : array-like
            Array containing the amplitudes of synaptic responses.

        Returns:
        --------
        max_amp : float
            The maximum or minimum amplitude based on the sign of the response.
        """
        max_amp = max(amp)
        min_amp = min(amp)
        if(abs(min_amp) > max_amp):
            return min_amp * 1000 # scale unit
        return max_amp * 1000 # scale unit


    def _calc_ppr_induction_recovery(self, amp, normalize_by_trial=True, print_math=True):
        """
        Calculates paired-pulse ratio, induction, and recovery metrics from response amplitudes.

        Parameters:
        -----------
        amp : array-like
            Array containing the amplitudes of synaptic responses to a pulse train.
        normalize_by_trial : bool, optional
            If True, normalize the amplitudes within each trial. Default is True.
        print_math : bool, optional
            If True, print detailed calculation steps and explanations. Default is True.

        Returns:
        --------
        tuple
            A tuple containing:
            - ppr: Paired-pulse ratio (2nd pulse / 1st pulse)
            - induction: Measure of facilitation/depression during initial pulses
            - recovery: Measure of recovery after the delay period

        Notes:
        ------
        - PPR > 1 indicates facilitation, PPR < 1 indicates depression
        - Induction > 0 indicates facilitation, Induction < 0 indicates depression
        - Recovery compares the response after delay to the initial pulses
        """
        amp = np.array(amp)
        amp = (amp * 1000) # scale up
        amp = amp.reshape(-1, amp.shape[-1])
        maxamp = amp.max(axis=1 if normalize_by_trial else None)

        def format_array(arr):
            """Format an array to 2 significant figures for cleaner output."""
            return np.array2string(arr, precision=2, separator=', ', suppress_small=True)

        if print_math:
            print("\n" + "="*40)
            print(f"Short Term Plasticity Results for {self.train_freq}Hz with {self.train_delay} Delay")
            print("="*40)
            print("PPR: Above 1 is facilitating, below 1 is depressing.")
            print("Induction: Above 0 is facilitating, below 0 is depressing.")
            print("Recovery: A measure of how fast STP decays.\n")

            # PPR Calculation
            ppr = amp[:, 1:2] / amp[:, 0:1]
            print("Paired Pulse Response (PPR)")
            print("Calculation: 2nd pulse / 1st pulse")
            print(f"Values: ({format_array(amp[:, 1:2])}) / ({format_array(amp[:, 0:1])}) = {format_array(ppr)}\n")

            # Induction Calculation
            induction = np.mean((amp[:, 5:8].mean(axis=1) - amp[:, :1].mean(axis=1)) / maxamp)
            print("Induction")
            print("Calculation: (avg(6th, 7th, 8th pulses) - 1st pulse) / max amps")
            print(f"Values: avg({format_array(amp[:, 5:8])}) - {format_array(amp[:, :1])} / {format_array(maxamp)}")
            print(f"({format_array(amp[:, 5:8].mean(axis=1))}) - ({format_array(amp[:, :1].mean(axis=1))}) / {format_array(maxamp)} = {induction:.3f}\n")

            # Recovery Calculation
            recovery = np.mean((amp[:, 8:12].mean(axis=1) - amp[:, :4].mean(axis=1)) / maxamp)
            print("Recovery")
            print("Calculation: (avg(9th, 10th, 11th, 12th pulses) - avg(1st to 4th pulses)) / max amps")
            print(f"Values: avg({format_array(amp[:, 8:12])}) - avg({format_array(amp[:, :4])}) / {format_array(maxamp)}")
            print(f"({format_array(amp[:, 8:12].mean(axis=1))}) - ({format_array(amp[:, :4].mean(axis=1))}) / {format_array(maxamp)} = {recovery:.3f}\n")

            print("="*40 + "\n")

        recovery = np.mean((amp[:, 8:12].mean(axis=1) - amp[:, :4].mean(axis=1)) / maxamp)
        induction = np.mean((amp[:, 5:8].mean(axis=1) - amp[:, :1].mean(axis=1)) / maxamp)
        ppr = amp[:, 1:2] / amp[:, 0:1]
        # maxamp = max(amp, key=lambda x: abs(x[0]))
        maxamp = maxamp.max()

        return ppr, induction, recovery


    def _set_syn_prop(self, **kwargs):
        """
        Sets the synaptic parameters based on user inputs from sliders.

        Parameters:
        -----------
        **kwargs : dict
            Synaptic properties (such as weight, Use, tau_f, tau_d) as keyword arguments.
        """
        for key, value in kwargs.items():
            setattr(self.syn, key, value)


    def _simulate_model(self,input_frequency, delay, vclamp=None):
        """
        Runs the simulation with the specified input frequency, delay, and voltage clamp settings.

        Parameters:
        -----------
        input_frequency : float
            Frequency of the input drive train in Hz.
        delay : float
            Delay period in milliseconds between the 8th and 9th pulses.
        vclamp : bool or None, optional
            Whether to use voltage clamp. If None, the current setting is used. Default is None.

        Notes:
        ------
        This method handles two different input modes:
        - Standard train mode with 8 initial pulses followed by a delay and 4 additional pulses
        - Continuous input mode where stimulation continues for a specified duration
        """
        if self.input_mode == False:
            self.tstop = self._set_drive_train(input_frequency, delay)
            h.tstop = self.tstop

            vcldur = [[0, 0, 0], [self.general_settings['tstart'], self.tstop, 1e9]]
            for i in range(3):
                self.vcl.amp[i] = self.conn['spec_settings']['vclamp_amp']
                self.vcl.dur[i] = vcldur[1][i]
            h.finitialize(self.cell.Vinit * mV)
            h.continuerun(self.tstop * ms)
        else:
            self.tstop = self.general_settings['tstart'] + self.general_settings['tdur']
            self.nstim.interval = 1000 / input_frequency
            self.nstim.number = np.ceil(self.w_duration.value / 1000 * input_frequency + 1)
            self.nstim2.number = 0
            self.tstop = self.w_duration.value + self.general_settings['tstart']

            h.finitialize(self.cell.Vinit * mV)
            h.continuerun(self.tstop * ms)


    def InteractiveTuner(self):
        """
        Sets up interactive sliders for tuning short-term plasticity (STP) parameters in a Jupyter Notebook.

        This method creates an interactive UI with sliders for:
        - Input frequency
        - Delay between pulse trains
        - Duration of stimulation (for continuous input mode)
        - Synaptic parameters (e.g., Use, tau_f, tau_d) based on the syn model

        It also provides buttons for:
        - Running a single event simulation
        - Running a train input simulation
        - Toggling voltage clamp mode
        - Switching between standard and continuous input modes

        Notes:
        ------
        Ideal for exploratory parameter tuning and interactive visualization of 
        synapse behavior with different parameter values and stimulation protocols.
        """
        # Widgets setup (Sliders)
        freqs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 35, 50, 100, 200]
        delays = [125, 250, 500, 1000, 2000, 4000]
        durations = [300, 500, 1000, 2000, 5000, 10000]
        freq0 = 50
        delay0 = 250
        duration0 = 300
        vlamp_status = self.vclamp

        w_run = widgets.Button(description='Run Train', icon='history', button_style='primary')
        w_single = widgets.Button(description='Single Event', icon='check', button_style='success')
        w_vclamp = widgets.ToggleButton(value=vlamp_status, description='Voltage Clamp', icon='fast-backward', button_style='warning')
        w_input_mode = widgets.ToggleButton(value=False, description='Continuous input', icon='eject', button_style='info')
        w_input_freq = widgets.SelectionSlider(options=freqs, value=freq0, description='Input Freq')


        # Sliders for delay and duration
        self.w_delay = widgets.SelectionSlider(options=delays, value=delay0, description='Delay')
        self.w_duration = widgets.SelectionSlider(options=durations, value=duration0, description='Duration')

        # Generate sliders dynamically based on valid numeric entries in self.slider_vars
        self.dynamic_sliders = {}
        print("Setting up slider! The sliders ranges are set by their init value so try changing that if you dont like the slider range!")
        for key, value in self.slider_vars.items():
            if isinstance(value, (int, float)):  # Only create sliders for numeric values
                if hasattr(self.syn, key):
                    if value == 0:
                        print(f'{key} was set to zero, going to try to set a range of values, try settings the {key} to a nonzero value if you dont like the range!')
                        slider = widgets.FloatSlider(value=value, min=0, max=1000, step=1, description=key)
                    else:
                        slider = widgets.FloatSlider(value=value, min=0, max=value*20, step=value/5, description=key)
                    self.dynamic_sliders[key] = slider
                else:
                    print(f"skipping slider for {key} due to not being a synaptic variable")

        def run_single_event(*args):
            clear_output()
            display(ui)
            self.vclamp = w_vclamp.value
            # Update synaptic properties based on slider values
            self.ispk=None
            self.SingleEvent()

        # Function to update UI based on input mode
        def update_ui(*args):
            clear_output()
            display(ui)
            self.vclamp = w_vclamp.value
            self.input_mode = w_input_mode.value
            syn_props = {var: slider.value for var, slider in self.dynamic_sliders.items()}
            self._set_syn_prop(**syn_props)
            if self.input_mode == False:
                self._simulate_model(w_input_freq.value, self.w_delay.value, w_vclamp.value)
            else:
                self._simulate_model(w_input_freq.value, self.w_duration.value, w_vclamp.value)
            amp = self._response_amplitude()
            self._plot_model([self.general_settings['tstart'] - self.nstim.interval / 3, self.tstop])
            _ = self._calc_ppr_induction_recovery(amp)

        # Function to switch between delay and duration sliders
        def switch_slider(*args):
            if w_input_mode.value:
                self.w_delay.layout.display = 'none'  # Hide delay slider
                self.w_duration.layout.display = ''   # Show duration slider
            else:
                self.w_delay.layout.display = ''      # Show delay slider
                self.w_duration.layout.display = 'none'  # Hide duration slider

        # Link input mode to slider switch
        w_input_mode.observe(switch_slider, names='value')

        # Hide the duration slider initially until the user selects it
        self.w_duration.layout.display = 'none'  # Hide duration slider

        w_single.on_click(run_single_event)
        w_run.on_click(update_ui)

        # Add the dynamic sliders to the UI
        slider_widgets = [slider for slider in self.dynamic_sliders.values()]

        button_row = HBox([w_run, w_single, w_vclamp, w_input_mode])
        slider_row = HBox([w_input_freq, self.w_delay, self.w_duration])

        half = len(slider_widgets) // 2
        col1 = VBox(slider_widgets[:half])
        col2 = VBox(slider_widgets[half:])
        slider_columns = HBox([col1, col2])

        ui = VBox([button_row, slider_row, slider_columns])

        display(ui)
        update_ui()


    def stp_frequency_response(self, freqs=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 35, 50, 100, 200], 
                                delay=250, plot=True,log_plot=True):
        """
        Analyze synaptic response across different stimulation frequencies.

        This method systematically tests how the synapse model responds to different 
        stimulation frequencies, calculating key short-term plasticity (STP) metrics
        for each frequency.

        Parameters:
        -----------
        freqs : list, optional
            List of frequencies to analyze (in Hz). Default covers a wide range from 1-200 Hz.
        delay : float, optional
            Delay between pulse trains in ms. Default is 250 ms.
        plot : bool, optional
            Whether to plot the results. Default is True.
        log_plot : bool, optional
            Whether to use logarithmic scale for frequency axis. Default is True.

        Returns:
        --------
        dict
            Dictionary containing frequency-dependent metrics with keys:
            - 'frequencies': List of tested frequencies
            - 'ppr': Paired-pulse ratios at each frequency
            - 'induction': Induction values at each frequency
            - 'recovery': Recovery values at each frequency

        Notes:
        ------
        This method is particularly useful for characterizing the frequency-dependent 
        behavior of synapses, such as identifying facilitating vs. depressing regimes
        or the frequency at which a synapse transitions between these behaviors.
        """
        results = {
            'frequencies': freqs,
            'ppr': [],
            'induction': [],
            'recovery': []
        }

        # Store original state
        original_ispk = self.ispk

        for freq in tqdm(freqs, desc="Analyzing frequencies"):
            self._simulate_model(freq, delay)
            amp = self._response_amplitude()
            ppr, induction, recovery = self._calc_ppr_induction_recovery(amp, print_math=False)

            results['ppr'].append(float(ppr))
            results['induction'].append(float(induction))
            results['recovery'].append(float(recovery))

        # Restore original state
        self.ispk = original_ispk

        if plot:
            self._plot_frequency_analysis(results,log_plot=log_plot)

        return results


    def _plot_frequency_analysis(self, results, log_plot):
        """
        Plot the frequency-dependent synaptic properties.

        Parameters:
        -----------
        results : dict
            Dictionary containing frequency analysis results with keys:
            - 'frequencies': List of tested frequencies
            - 'ppr': Paired-pulse ratios at each frequency
            - 'induction': Induction values at each frequency
            - 'recovery': Recovery values at each frequency
        log_plot : bool
            Whether to use logarithmic scale for frequency axis

        Notes:
        ------
        Creates a figure with three subplots showing:
        1. Paired-pulse ratio vs. frequency
        2. Induction vs. frequency
        3. Recovery vs. frequency

        Each plot includes a horizontal reference line at y=0 or y=1 to indicate
        the boundary between facilitation and depression.
        """
        fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))


        # Plot PPR
        if log_plot:
            ax1.semilogx(results['frequencies'], results['ppr'], 'o-')
        else:
            ax1.plot(results['frequencies'], results['ppr'], 'o-')
        ax1.axhline(y=1, color='gray', linestyle='--', alpha=0.5)
        ax1.set_xlabel('Frequency (Hz)')
        ax1.set_ylabel('Paired Pulse Ratio')
        ax1.set_title('PPR vs Frequency')
        ax1.grid(True)

        # Plot Induction
        if log_plot:
            ax2.semilogx(results['frequencies'], results['induction'], 'o-')
        else:
            ax2.plot(results['frequencies'], results['induction'], 'o-')
        ax2.axhline(y=0, color='gray', linestyle='--', alpha=0.5)
        ax2.set_xlabel('Frequency (Hz)')
        ax2.set_ylabel('Induction')
        ax2.set_title('Induction vs Frequency')
        ax2.grid(True)

        # Plot Recovery
        if log_plot:
            ax3.semilogx(results['frequencies'], results['recovery'], 'o-')
        else:
            ax3.plot(results['frequencies'], results['recovery'], 'o-')
        ax3.axhline(y=0, color='gray', linestyle='--', alpha=0.5)
        ax3.set_xlabel('Frequency (Hz)')
        ax3.set_ylabel('Recovery')
        ax3.set_title('Recovery vs Frequency')
        ax3.grid(True)

        plt.tight_layout()
        plt.show()

__init__(mechanisms_dir, templates_dir, conn_type_settings, connection, general_settings, json_folder_path=None, current_name='i', other_vars_to_record=None, slider_vars=None)

Initialize the SynapseModule class with connection type settings, mechanisms, and template directories.

Parameters:

mechanisms_dir : str Directory path containing the compiled mod files needed for NEURON mechanisms. templates_dir : str Directory path containing cell template files (.hoc or .py) loaded into NEURON. conn_type_settings : dict A dictionary containing connection-specific settings, such as synaptic properties and details. connection : str Name of the connection type to be used from the conn_type_settings dictionary. general_settings : dict General settings dictionary including parameters like simulation time step, duration, and temperature. json_folder_path : str, optional Path to folder containing JSON files with additional synaptic properties to update settings. current_name : str, optional Name of the synaptic current variable to be recorded (default is 'i'). other_vars_to_record : list, optional List of additional synaptic variables to record during the simulation (e.g., 'Pr', 'Use'). slider_vars : list, optional List of synaptic variables you would like sliders set up for the STP sliders method by default will use all parameters in spec_syn_param.

Source code in bmtool/synapses.py

def __init__(self, mechanisms_dir: str, templates_dir: str, conn_type_settings: dict, connection: str,
             general_settings: dict, json_folder_path: str = None, current_name: str = 'i',
             other_vars_to_record: list = None, slider_vars: list = None) -> None:
    """
    Initialize the SynapseModule class with connection type settings, mechanisms, and template directories.

    Parameters:
    -----------
    mechanisms_dir : str
        Directory path containing the compiled mod files needed for NEURON mechanisms.
    templates_dir : str
        Directory path containing cell template files (.hoc or .py) loaded into NEURON.
    conn_type_settings : dict
        A dictionary containing connection-specific settings, such as synaptic properties and details.
    connection : str
        Name of the connection type to be used from the conn_type_settings dictionary.
    general_settings : dict
        General settings dictionary including parameters like simulation time step, duration, and temperature.
    json_folder_path : str, optional
        Path to folder containing JSON files with additional synaptic properties to update settings.
    current_name : str, optional
        Name of the synaptic current variable to be recorded (default is 'i').
    other_vars_to_record : list, optional
        List of additional synaptic variables to record during the simulation (e.g., 'Pr', 'Use').
    slider_vars : list, optional
        List of synaptic variables you would like sliders set up for the STP sliders method by default will use all parameters in spec_syn_param.

    """
    neuron.load_mechanisms(mechanisms_dir)
    h.load_file(templates_dir)
    self.conn_type_settings = conn_type_settings
    if json_folder_path:
        print(f"updating settings from json path {json_folder_path}")
        self._update_spec_syn_param(json_folder_path)
    self.general_settings = general_settings
    self.conn = self.conn_type_settings[connection]
    self.synaptic_props = self.conn['spec_syn_param']
    self.vclamp = general_settings['vclamp']
    self.current_name = current_name
    self.other_vars_to_record = other_vars_to_record
    self.ispk = None

    if slider_vars:
        # Start by filtering based on keys in slider_vars
        self.slider_vars = {key: value for key, value in self.synaptic_props.items() if key in slider_vars}
        # Iterate over slider_vars and check for missing keys in self.synaptic_props
        for key in slider_vars:
            # If the key is missing from synaptic_props, get the value using getattr
            if key not in self.synaptic_props:
                try:
                    # Get the alternative value from getattr dynamically
                    self._set_up_cell()
                    self._set_up_synapse()
                    value = getattr(self.syn,key)
                    #print(value)
                    self.slider_vars[key] = value
                except AttributeError as e:
                    print(f"Error accessing '{key}' in syn {self.syn}: {e}")

    else:
        self.slider_vars = self.synaptic_props


    h.tstop = general_settings['tstart'] + general_settings['tdur']
    h.dt = general_settings['dt']  # Time step (resolution) of the simulation in ms
    h.steps_per_ms = 1 / h.dt
    h.celsius = general_settings['celsius']

    # get some stuff set up we need for both SingleEvent and Interactive Tuner
    self._set_up_cell()
    self._set_up_synapse()

    self.nstim = h.NetStim()
    self.nstim2 = h.NetStim()

    self.vcl = h.VClamp(self.cell.soma[0](0.5))

    self.nc = h.NetCon(self.nstim, self.syn, self.general_settings['threshold'], self.general_settings['delay'], self.general_settings['weight'])
    self.nc2 = h.NetCon(self.nstim2, self.syn, self.general_settings['threshold'], self.general_settings['delay'], self.general_settings['weight'])

    self._set_up_recorders()

_update_spec_syn_param(json_folder_path)

Update specific synaptic parameters using JSON files located in the specified folder.

Parameters:

json_folder_path : str Path to folder containing JSON files, where each JSON file corresponds to a connection type.

Source code in bmtool/synapses.py

def _update_spec_syn_param(self, json_folder_path):
    """
    Update specific synaptic parameters using JSON files located in the specified folder.

    Parameters:
    -----------
    json_folder_path : str
        Path to folder containing JSON files, where each JSON file corresponds to a connection type.
    """
    for conn_type, settings in self.conn_type_settings.items():
        json_file_path = os.path.join(json_folder_path, f"{conn_type}.json")
        if os.path.exists(json_file_path):
            with open(json_file_path, 'r') as json_file:
                json_data = json.load(json_file)
                settings['spec_syn_param'].update(json_data)
        else:
            print(f"JSON file for {conn_type} not found.")

_set_up_cell()

Set up the neuron cell based on the specified connection settings.

Source code in bmtool/synapses.py

def _set_up_cell(self):
    """
    Set up the neuron cell based on the specified connection settings.
    """
    self.cell = getattr(h, self.conn['spec_settings']['post_cell'])()

_set_up_synapse()

Set up the synapse on the target cell according to the synaptic parameters in conn_type_settings.

Notes:

• _set_up_cell() should be called before setting up the synapse.
• Synapse location, type, and properties are specified within spec_syn_param and spec_settings.

Source code in bmtool/synapses.py

def _set_up_synapse(self):
    """
    Set up the synapse on the target cell according to the synaptic parameters in `conn_type_settings`.

    Notes:
    ------
    - `_set_up_cell()` should be called before setting up the synapse.
    - Synapse location, type, and properties are specified within `spec_syn_param` and `spec_settings`.
    """
    self.syn = getattr(h, self.conn['spec_settings']['level_of_detail'])(list(self.cell.all)[self.conn['spec_settings']['sec_id']](self.conn['spec_settings']['sec_x']))
    for key, value in self.conn['spec_syn_param'].items():
        if isinstance(value, (int, float)):  # Only create sliders for numeric values
            if hasattr(self.syn, key):
                setattr(self.syn, key, value)
            else:
                print(f"Warning: {key} cannot be assigned as it does not exist in the synapse. Check your mod file or spec_syn_param.")

_set_up_recorders()

Set up recording vectors to capture simulation data.

The method sets up recorders for: - Synaptic current specified by current_name - Other specified synaptic variables (other_vars_to_record) - Time, soma voltage, and voltage clamp current for all simulations.

Source code in bmtool/synapses.py

def _set_up_recorders(self):
    """
    Set up recording vectors to capture simulation data.

    The method sets up recorders for:
    - Synaptic current specified by `current_name`
    - Other specified synaptic variables (`other_vars_to_record`)
    - Time, soma voltage, and voltage clamp current for all simulations.
    """
    self.rec_vectors = {}
    for var in self.other_vars_to_record:
        self.rec_vectors[var] = h.Vector()
        ref_attr = f'_ref_{var}'
        if hasattr(self.syn, ref_attr):
            self.rec_vectors[var].record(getattr(self.syn, ref_attr))
        else:
            print(f"Warning: {ref_attr} not found in the syn object. Use vars() to inspect available attributes.")

    # Record synaptic current
    self.rec_vectors[self.current_name] = h.Vector()
    ref_attr = f'_ref_{self.current_name}'
    if hasattr(self.syn, ref_attr):
        self.rec_vectors[self.current_name].record(getattr(self.syn, ref_attr))
    else:
        print("Warning: Synaptic current recorder not set up correctly.")

    # Record time, synaptic events, soma voltage, and voltage clamp current
    self.t = h.Vector()
    self.tspk = h.Vector()
    self.soma_v = h.Vector()
    self.ivcl = h.Vector()

    self.t.record(h._ref_t)
    self.nc.record(self.tspk)
    self.nc2.record(self.tspk)
    self.soma_v.record(self.cell.soma[0](0.5)._ref_v)
    self.ivcl.record(self.vcl._ref_i)

SingleEvent(plot_and_print=True)

Simulate a single synaptic event by delivering an input stimulus to the synapse.

The method sets up the neuron cell, synapse, stimulus, and voltage clamp, and then runs the NEURON simulation for a single event. The single synaptic event will occur at general_settings['tstart'] Will display graphs and synaptic properies works best with a jupyter notebook

Source code in bmtool/synapses.py

def SingleEvent(self,plot_and_print=True):
    """
    Simulate a single synaptic event by delivering an input stimulus to the synapse.

    The method sets up the neuron cell, synapse, stimulus, and voltage clamp, 
    and then runs the NEURON simulation for a single event. The single synaptic event will occur at general_settings['tstart']
    Will display graphs and synaptic properies works best with a jupyter notebook
    """
    self.ispk = None

    # user slider values if the sliders are set up
    if hasattr(self, 'dynamic_sliders'):
        syn_props = {var: slider.value for var, slider in self.dynamic_sliders.items()}
        self._set_syn_prop(**syn_props)

    # sets values based off optimizer 
    if hasattr(self,'using_optimizer'):
        for name, value in zip(self.param_names, self.params):
            setattr(self.syn, name, value)

    # Set up the stimulus
    self.nstim.start = self.general_settings['tstart']
    self.nstim.noise = 0
    self.nstim2.start = h.tstop
    self.nstim2.noise = 0

    # Set up voltage clamp
    vcldur = [[0, 0, 0], [self.general_settings['tstart'], h.tstop, 1e9]]
    for i in range(3):
        self.vcl.amp[i] = self.conn['spec_settings']['vclamp_amp']
        self.vcl.dur[i] = vcldur[1][i]

    # Run simulation
    h.tstop = self.general_settings['tstart'] + self.general_settings['tdur']
    self.nstim.interval = self.general_settings['tdur']
    self.nstim.number = 1
    self.nstim2.start = h.tstop
    h.run()

    current = np.array(self.rec_vectors[self.current_name])
    syn_props = self._get_syn_prop(rise_interval=self.general_settings['rise_interval'],dt=h.dt) 
    current = (current - syn_props['baseline']) * 1000  # Convert to pA
    current_integral = np.trapz(current, dx=h.dt)  # pA·ms

    if plot_and_print:
        self._plot_model([self.general_settings['tstart'] - 5, self.general_settings['tstart'] + self.general_settings['tdur']])    
        for prop in syn_props.items():
            print(prop)
        print(f'Current Integral in pA*ms: {current_integral:.2f}')

    self.rise_time = syn_props['rise_time']
    self.decay_time = syn_props['decay_time']

_get_syn_prop(rise_interval=(0.2, 0.8), dt=h.dt, short=False)

Calculate synaptic properties such as peak amplitude, latency, rise time, decay time, and half-width.

Parameters:

rise_interval : tuple of floats, optional Fractional rise time interval to calculate (default is (0.2, 0.8)). dt : float, optional Time step of the simulation (default is NEURON's h.dt). short : bool, optional If True, only return baseline and sign without calculating full properties.

Returns:

dict A dictionary containing the synaptic properties: baseline, sign, peak amplitude, latency, rise time, decay time, and half-width.

Source code in bmtool/synapses.py

def _get_syn_prop(self, rise_interval=(0.2, 0.8), dt=h.dt, short=False):
    """
    Calculate synaptic properties such as peak amplitude, latency, rise time, decay time, and half-width.

    Parameters:
    -----------
    rise_interval : tuple of floats, optional
        Fractional rise time interval to calculate (default is (0.2, 0.8)).
    dt : float, optional
        Time step of the simulation (default is NEURON's `h.dt`).
    short : bool, optional
        If True, only return baseline and sign without calculating full properties.

    Returns:
    --------
    dict
        A dictionary containing the synaptic properties: baseline, sign, peak amplitude, latency, rise time, 
        decay time, and half-width.
    """
    if self.vclamp:
        isyn = self.ivcl
    else:
        isyn = self.rec_vectors[self.current_name]
    isyn = np.asarray(isyn)
    tspk = np.asarray(self.tspk)
    if tspk.size:
        tspk = tspk[0]

    ispk = int(np.floor(tspk / dt))
    baseline = isyn[ispk]
    isyn = isyn[ispk:] - baseline
    # print(np.abs(isyn))
    # print(np.argmax(np.abs(isyn)))
    # print(isyn[np.argmax(np.abs(isyn))])
    # print(np.sign(isyn[np.argmax(np.abs(isyn))]))  
    sign = np.sign(isyn[np.argmax(np.abs(isyn))])  
    if short:
        return {'baseline': baseline, 'sign': sign}
    isyn *= sign
    # print(isyn)
    # peak amplitude
    ipk, _ = find_peaks(isyn)
    ipk = ipk[0]
    peak = isyn[ipk]
    # latency
    istart = self._find_first(np.diff(isyn[:ipk + 1]) > 0)
    latency = dt * (istart + 1)
    # rise time
    rt1 = self._find_first(isyn[istart:ipk + 1] > rise_interval[0] * peak)
    rt2 = self._find_first(isyn[istart:ipk + 1] > rise_interval[1] * peak)
    rise_time = (rt2 - rt1) * dt
    # decay time
    iend = self._find_first(np.diff(isyn[ipk:]) > 0)
    iend = isyn.size - 1 if iend is None else iend + ipk
    decay_len = iend - ipk + 1
    popt, _ = curve_fit(lambda t, a, tau: a * np.exp(-t / tau), dt * np.arange(decay_len),
                        isyn[ipk:iend + 1], p0=(peak, dt * decay_len / 2))
    decay_time = popt[1]
    # half-width
    hw1 = self._find_first(isyn[istart:ipk + 1] > 0.5 * peak)
    hw2 = self._find_first(isyn[ipk:] < 0.5 * peak)
    hw2 = isyn.size if hw2 is None else hw2 + ipk
    half_width = dt * (hw2 - hw1)
    output = {'baseline': baseline, 'sign': sign, 'latency': latency,
        'amp': peak, 'rise_time': rise_time, 'decay_time': decay_time, 'half_width': half_width}
    return output

_set_syn_prop(**kwargs)

Sets the synaptic parameters based on user inputs from sliders.

Parameters:

**kwargs : dict Synaptic properties (such as weight, Use, tau_f, tau_d) as keyword arguments.

Source code in bmtool/synapses.py

def _set_syn_prop(self, **kwargs):
    """
    Sets the synaptic parameters based on user inputs from sliders.

    Parameters:
    -----------
    **kwargs : dict
        Synaptic properties (such as weight, Use, tau_f, tau_d) as keyword arguments.
    """
    for key, value in kwargs.items():
        setattr(self.syn, key, value)

_simulate_model(input_frequency, delay, vclamp=None)

Runs the simulation with the specified input frequency, delay, and voltage clamp settings.

Parameters:

input_frequency : float Frequency of the input drive train in Hz. delay : float Delay period in milliseconds between the 8th and 9th pulses. vclamp : bool or None, optional Whether to use voltage clamp. If None, the current setting is used. Default is None.

Notes:

This method handles two different input modes: - Standard train mode with 8 initial pulses followed by a delay and 4 additional pulses - Continuous input mode where stimulation continues for a specified duration

Source code in bmtool/synapses.py

def _simulate_model(self,input_frequency, delay, vclamp=None):
    """
    Runs the simulation with the specified input frequency, delay, and voltage clamp settings.

    Parameters:
    -----------
    input_frequency : float
        Frequency of the input drive train in Hz.
    delay : float
        Delay period in milliseconds between the 8th and 9th pulses.
    vclamp : bool or None, optional
        Whether to use voltage clamp. If None, the current setting is used. Default is None.

    Notes:
    ------
    This method handles two different input modes:
    - Standard train mode with 8 initial pulses followed by a delay and 4 additional pulses
    - Continuous input mode where stimulation continues for a specified duration
    """
    if self.input_mode == False:
        self.tstop = self._set_drive_train(input_frequency, delay)
        h.tstop = self.tstop

        vcldur = [[0, 0, 0], [self.general_settings['tstart'], self.tstop, 1e9]]
        for i in range(3):
            self.vcl.amp[i] = self.conn['spec_settings']['vclamp_amp']
            self.vcl.dur[i] = vcldur[1][i]
        h.finitialize(self.cell.Vinit * mV)
        h.continuerun(self.tstop * ms)
    else:
        self.tstop = self.general_settings['tstart'] + self.general_settings['tdur']
        self.nstim.interval = 1000 / input_frequency
        self.nstim.number = np.ceil(self.w_duration.value / 1000 * input_frequency + 1)
        self.nstim2.number = 0
        self.tstop = self.w_duration.value + self.general_settings['tstart']

        h.finitialize(self.cell.Vinit * mV)
        h.continuerun(self.tstop * ms)

InteractiveTuner()

Sets up interactive sliders for tuning short-term plasticity (STP) parameters in a Jupyter Notebook.

This method creates an interactive UI with sliders for: - Input frequency - Delay between pulse trains - Duration of stimulation (for continuous input mode) - Synaptic parameters (e.g., Use, tau_f, tau_d) based on the syn model

It also provides buttons for: - Running a single event simulation - Running a train input simulation - Toggling voltage clamp mode - Switching between standard and continuous input modes

Notes:

Ideal for exploratory parameter tuning and interactive visualization of synapse behavior with different parameter values and stimulation protocols.

Source code in bmtool/synapses.py

def InteractiveTuner(self):
    """
    Sets up interactive sliders for tuning short-term plasticity (STP) parameters in a Jupyter Notebook.

    This method creates an interactive UI with sliders for:
    - Input frequency
    - Delay between pulse trains
    - Duration of stimulation (for continuous input mode)
    - Synaptic parameters (e.g., Use, tau_f, tau_d) based on the syn model

    It also provides buttons for:
    - Running a single event simulation
    - Running a train input simulation
    - Toggling voltage clamp mode
    - Switching between standard and continuous input modes

    Notes:
    ------
    Ideal for exploratory parameter tuning and interactive visualization of 
    synapse behavior with different parameter values and stimulation protocols.
    """
    # Widgets setup (Sliders)
    freqs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 35, 50, 100, 200]
    delays = [125, 250, 500, 1000, 2000, 4000]
    durations = [300, 500, 1000, 2000, 5000, 10000]
    freq0 = 50
    delay0 = 250
    duration0 = 300
    vlamp_status = self.vclamp

    w_run = widgets.Button(description='Run Train', icon='history', button_style='primary')
    w_single = widgets.Button(description='Single Event', icon='check', button_style='success')
    w_vclamp = widgets.ToggleButton(value=vlamp_status, description='Voltage Clamp', icon='fast-backward', button_style='warning')
    w_input_mode = widgets.ToggleButton(value=False, description='Continuous input', icon='eject', button_style='info')
    w_input_freq = widgets.SelectionSlider(options=freqs, value=freq0, description='Input Freq')


    # Sliders for delay and duration
    self.w_delay = widgets.SelectionSlider(options=delays, value=delay0, description='Delay')
    self.w_duration = widgets.SelectionSlider(options=durations, value=duration0, description='Duration')

    # Generate sliders dynamically based on valid numeric entries in self.slider_vars
    self.dynamic_sliders = {}
    print("Setting up slider! The sliders ranges are set by their init value so try changing that if you dont like the slider range!")
    for key, value in self.slider_vars.items():
        if isinstance(value, (int, float)):  # Only create sliders for numeric values
            if hasattr(self.syn, key):
                if value == 0:
                    print(f'{key} was set to zero, going to try to set a range of values, try settings the {key} to a nonzero value if you dont like the range!')
                    slider = widgets.FloatSlider(value=value, min=0, max=1000, step=1, description=key)
                else:
                    slider = widgets.FloatSlider(value=value, min=0, max=value*20, step=value/5, description=key)
                self.dynamic_sliders[key] = slider
            else:
                print(f"skipping slider for {key} due to not being a synaptic variable")

    def run_single_event(*args):
        clear_output()
        display(ui)
        self.vclamp = w_vclamp.value
        # Update synaptic properties based on slider values
        self.ispk=None
        self.SingleEvent()

    # Function to update UI based on input mode
    def update_ui(*args):
        clear_output()
        display(ui)
        self.vclamp = w_vclamp.value
        self.input_mode = w_input_mode.value
        syn_props = {var: slider.value for var, slider in self.dynamic_sliders.items()}
        self._set_syn_prop(**syn_props)
        if self.input_mode == False:
            self._simulate_model(w_input_freq.value, self.w_delay.value, w_vclamp.value)
        else:
            self._simulate_model(w_input_freq.value, self.w_duration.value, w_vclamp.value)
        amp = self._response_amplitude()
        self._plot_model([self.general_settings['tstart'] - self.nstim.interval / 3, self.tstop])
        _ = self._calc_ppr_induction_recovery(amp)

    # Function to switch between delay and duration sliders
    def switch_slider(*args):
        if w_input_mode.value:
            self.w_delay.layout.display = 'none'  # Hide delay slider
            self.w_duration.layout.display = ''   # Show duration slider
        else:
            self.w_delay.layout.display = ''      # Show delay slider
            self.w_duration.layout.display = 'none'  # Hide duration slider

    # Link input mode to slider switch
    w_input_mode.observe(switch_slider, names='value')

    # Hide the duration slider initially until the user selects it
    self.w_duration.layout.display = 'none'  # Hide duration slider

    w_single.on_click(run_single_event)
    w_run.on_click(update_ui)

    # Add the dynamic sliders to the UI
    slider_widgets = [slider for slider in self.dynamic_sliders.values()]

    button_row = HBox([w_run, w_single, w_vclamp, w_input_mode])
    slider_row = HBox([w_input_freq, self.w_delay, self.w_duration])

    half = len(slider_widgets) // 2
    col1 = VBox(slider_widgets[:half])
    col2 = VBox(slider_widgets[half:])
    slider_columns = HBox([col1, col2])

    ui = VBox([button_row, slider_row, slider_columns])

    display(ui)
    update_ui()

stp_frequency_response(freqs=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 35, 50, 100, 200], delay=250, plot=True, log_plot=True)

Analyze synaptic response across different stimulation frequencies.

This method systematically tests how the synapse model responds to different stimulation frequencies, calculating key short-term plasticity (STP) metrics for each frequency.

Parameters:

freqs : list, optional List of frequencies to analyze (in Hz). Default covers a wide range from 1-200 Hz. delay : float, optional Delay between pulse trains in ms. Default is 250 ms. plot : bool, optional Whether to plot the results. Default is True. log_plot : bool, optional Whether to use logarithmic scale for frequency axis. Default is True.

Returns:

dict Dictionary containing frequency-dependent metrics with keys: - 'frequencies': List of tested frequencies - 'ppr': Paired-pulse ratios at each frequency - 'induction': Induction values at each frequency - 'recovery': Recovery values at each frequency

Notes:

This method is particularly useful for characterizing the frequency-dependent behavior of synapses, such as identifying facilitating vs. depressing regimes or the frequency at which a synapse transitions between these behaviors.

Source code in bmtool/synapses.py

def stp_frequency_response(self, freqs=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 35, 50, 100, 200], 
                            delay=250, plot=True,log_plot=True):
    """
    Analyze synaptic response across different stimulation frequencies.

    This method systematically tests how the synapse model responds to different 
    stimulation frequencies, calculating key short-term plasticity (STP) metrics
    for each frequency.

    Parameters:
    -----------
    freqs : list, optional
        List of frequencies to analyze (in Hz). Default covers a wide range from 1-200 Hz.
    delay : float, optional
        Delay between pulse trains in ms. Default is 250 ms.
    plot : bool, optional
        Whether to plot the results. Default is True.
    log_plot : bool, optional
        Whether to use logarithmic scale for frequency axis. Default is True.

    Returns:
    --------
    dict
        Dictionary containing frequency-dependent metrics with keys:
        - 'frequencies': List of tested frequencies
        - 'ppr': Paired-pulse ratios at each frequency
        - 'induction': Induction values at each frequency
        - 'recovery': Recovery values at each frequency

    Notes:
    ------
    This method is particularly useful for characterizing the frequency-dependent 
    behavior of synapses, such as identifying facilitating vs. depressing regimes
    or the frequency at which a synapse transitions between these behaviors.
    """
    results = {
        'frequencies': freqs,
        'ppr': [],
        'induction': [],
        'recovery': []
    }

    # Store original state
    original_ispk = self.ispk

    for freq in tqdm(freqs, desc="Analyzing frequencies"):
        self._simulate_model(freq, delay)
        amp = self._response_amplitude()
        ppr, induction, recovery = self._calc_ppr_induction_recovery(amp, print_math=False)

        results['ppr'].append(float(ppr))
        results['induction'].append(float(induction))
        results['recovery'].append(float(recovery))

    # Restore original state
    self.ispk = original_ispk

    if plot:
        self._plot_frequency_analysis(results,log_plot=log_plot)

    return results

Gap Junction Tuning

bmtool.synapses.GapJunctionTuner

Source code in bmtool/synapses.py

class GapJunctionTuner:
    def __init__(self, mechanisms_dir: str, templates_dir: str, general_settings: dict, conn_type_settings: dict):
        """
        Initialize the GapJunctionTuner class.

        Parameters:
        -----------
        mechanisms_dir : str
            Directory path containing the compiled mod files needed for NEURON mechanisms.
        templates_dir : str
            Directory path containing cell template files (.hoc or .py) loaded into NEURON.
        general_settings : dict
            General settings dictionary including parameters like simulation time step, duration, and temperature.
        conn_type_settings : dict
            A dictionary containing connection-specific settings for gap junctions.
        """
        neuron.load_mechanisms(mechanisms_dir)
        h.load_file(templates_dir)

        self.general_settings = general_settings
        self.conn_type_settings = conn_type_settings

        h.tstop = general_settings['tstart'] + general_settings['tdur'] + 100.
        h.dt = general_settings['dt']  # Time step (resolution) of the simulation in ms
        h.steps_per_ms = 1 / h.dt
        h.celsius = general_settings['celsius']

        self.cell_name = conn_type_settings['cell']

        # set up gap junctions
        pc = h.ParallelContext()

        self.cell1 = getattr(h, self.cell_name)()
        self.cell2 = getattr(h, self.cell_name)()

        self.icl = h.IClamp(self.cell1.soma[0](0.5))
        self.icl.delay = self.general_settings['tstart'] 
        self.icl.dur = self.general_settings['tdur'] 
        self.icl.amp = self.conn_type_settings['iclamp_amp'] # nA

        sec1 = list(self.cell1.all)[conn_type_settings['sec_id']]
        sec2 = list(self.cell2.all)[conn_type_settings['sec_id']]

        pc.source_var(sec1(conn_type_settings['sec_x'])._ref_v, 0, sec=sec1)
        self.gap_junc_1 = h.Gap(sec1(0.5))
        pc.target_var(self.gap_junc_1 ._ref_vgap, 1)

        pc.source_var(sec2(conn_type_settings['sec_x'])._ref_v, 1, sec=sec2)
        self.gap_junc_2 = h.Gap(sec2(0.5))
        pc.target_var(self.gap_junc_2._ref_vgap, 0)

        pc.setup_transfer()

    def model(self,resistance):
        """
        Run a simulation with a specified gap junction resistance.

        Parameters:
        -----------
        resistance : float
            The gap junction resistance value (in MOhm) to use for the simulation.

        Notes:
        ------
        This method sets up the gap junction resistance, initializes recording vectors for time
        and membrane voltages of both cells, and runs the NEURON simulation.
        """
        self.gap_junc_1.g = resistance
        self.gap_junc_2.g = resistance

        t_vec = h.Vector()
        soma_v_1 = h.Vector()
        soma_v_2 = h.Vector()
        t_vec.record(h._ref_t)
        soma_v_1.record(self.cell1.soma[0](0.5)._ref_v)
        soma_v_2.record(self.cell2.soma[0](0.5)._ref_v)

        self.t_vec = t_vec
        self.soma_v_1 = soma_v_1
        self.soma_v_2 = soma_v_2

        h.finitialize(-70 * mV)
        h.continuerun(h.tstop * ms)


    def plot_model(self):
        """
        Plot the voltage traces of both cells to visualize gap junction coupling.

        This method creates a plot showing the membrane potential of both cells over time,
        highlighting the effect of gap junction coupling when a current step is applied to cell 1.
        """
        t_range = [self.general_settings['tstart'] - 100., self.general_settings['tstart']+self.general_settings['tdur'] + 100.]
        t = np.array(self.t_vec)
        v1 = np.array(self.soma_v_1)
        v2 = np.array(self.soma_v_2)
        tidx = (t >= t_range[0]) & (t <= t_range[1])

        plt.figure()
        plt.plot(t[tidx], v1[tidx], 'b', label=f'{self.cell_name} 1')
        plt.plot(t[tidx], v2[tidx], 'r', label=f'{self.cell_name} 2')
        plt.title(f"{self.cell_name} gap junction")
        plt.xlabel('Time (ms)')
        plt.ylabel('Membrane Voltage (mV)')
        plt.legend()
        plt.show()        


    def coupling_coefficient(self,t, v1, v2, t_start, t_end, dt=h.dt):
        """
        Calculate the coupling coefficient between two cells connected by a gap junction.

        Parameters:
        -----------
        t : array-like
            Time vector.
        v1 : array-like
            Voltage trace of the cell receiving the current injection.
        v2 : array-like
            Voltage trace of the coupled cell.
        t_start : float
            Start time for calculating the steady-state voltage change.
        t_end : float
            End time for calculating the steady-state voltage change.
        dt : float, optional
            Time step of the simulation. Default is h.dt.

        Returns:
        --------
        float
            The coupling coefficient, defined as the ratio of voltage change in cell 2 
            to voltage change in cell 1 (ΔV₂/ΔV₁).
        """
        t = np.asarray(t)
        v1 = np.asarray(v1)
        v2 = np.asarray(v2)
        idx1 = np.nonzero(t < t_start)[0][-1]
        idx2 = np.nonzero(t < t_end)[0][-1]
        return (v2[idx2] - v2[idx1]) / (v1[idx2] - v1[idx1])


    def InteractiveTuner(self):
        w_run = widgets.Button(description='Run', icon='history', button_style='primary')
        values = [i * 10**-4 for i in range(1, 101)]  # From 1e-4 to 1e-2

        # Create the SelectionSlider widget with appropriate formatting
        resistance = widgets.SelectionSlider(
            options=[("%g"%i,i) for i in values],  # Use scientific notation for display
            value=10**-3,  # Default value
            description='Resistance: ',
            continuous_update=True
)

        ui = VBox([w_run,resistance])
        display(ui)
        def on_button(*args):
            clear_output()
            display(ui)
            resistance_for_gap = resistance.value
            self.model(resistance_for_gap)
            self.plot_model()
            cc = self.coupling_coefficient(self.t_vec, self.soma_v_1, self.soma_v_2, 500, 1000)
            print(f"coupling_coefficient is {cc:0.4f}")

        on_button()    
        w_run.on_click(on_button)

__init__(mechanisms_dir, templates_dir, general_settings, conn_type_settings)

Initialize the GapJunctionTuner class.

Parameters:

mechanisms_dir : str Directory path containing the compiled mod files needed for NEURON mechanisms. templates_dir : str Directory path containing cell template files (.hoc or .py) loaded into NEURON. general_settings : dict General settings dictionary including parameters like simulation time step, duration, and temperature. conn_type_settings : dict A dictionary containing connection-specific settings for gap junctions.

Source code in bmtool/synapses.py

def __init__(self, mechanisms_dir: str, templates_dir: str, general_settings: dict, conn_type_settings: dict):
    """
    Initialize the GapJunctionTuner class.

    Parameters:
    -----------
    mechanisms_dir : str
        Directory path containing the compiled mod files needed for NEURON mechanisms.
    templates_dir : str
        Directory path containing cell template files (.hoc or .py) loaded into NEURON.
    general_settings : dict
        General settings dictionary including parameters like simulation time step, duration, and temperature.
    conn_type_settings : dict
        A dictionary containing connection-specific settings for gap junctions.
    """
    neuron.load_mechanisms(mechanisms_dir)
    h.load_file(templates_dir)

    self.general_settings = general_settings
    self.conn_type_settings = conn_type_settings

    h.tstop = general_settings['tstart'] + general_settings['tdur'] + 100.
    h.dt = general_settings['dt']  # Time step (resolution) of the simulation in ms
    h.steps_per_ms = 1 / h.dt
    h.celsius = general_settings['celsius']

    self.cell_name = conn_type_settings['cell']

    # set up gap junctions
    pc = h.ParallelContext()

    self.cell1 = getattr(h, self.cell_name)()
    self.cell2 = getattr(h, self.cell_name)()

    self.icl = h.IClamp(self.cell1.soma[0](0.5))
    self.icl.delay = self.general_settings['tstart'] 
    self.icl.dur = self.general_settings['tdur'] 
    self.icl.amp = self.conn_type_settings['iclamp_amp'] # nA

    sec1 = list(self.cell1.all)[conn_type_settings['sec_id']]
    sec2 = list(self.cell2.all)[conn_type_settings['sec_id']]

    pc.source_var(sec1(conn_type_settings['sec_x'])._ref_v, 0, sec=sec1)
    self.gap_junc_1 = h.Gap(sec1(0.5))
    pc.target_var(self.gap_junc_1 ._ref_vgap, 1)

    pc.source_var(sec2(conn_type_settings['sec_x'])._ref_v, 1, sec=sec2)
    self.gap_junc_2 = h.Gap(sec2(0.5))
    pc.target_var(self.gap_junc_2._ref_vgap, 0)

    pc.setup_transfer()

model(resistance)

Run a simulation with a specified gap junction resistance.

Parameters:

resistance : float The gap junction resistance value (in MOhm) to use for the simulation.

Notes:

This method sets up the gap junction resistance, initializes recording vectors for time and membrane voltages of both cells, and runs the NEURON simulation.

Source code in bmtool/synapses.py

def model(self,resistance):
    """
    Run a simulation with a specified gap junction resistance.

    Parameters:
    -----------
    resistance : float
        The gap junction resistance value (in MOhm) to use for the simulation.

    Notes:
    ------
    This method sets up the gap junction resistance, initializes recording vectors for time
    and membrane voltages of both cells, and runs the NEURON simulation.
    """
    self.gap_junc_1.g = resistance
    self.gap_junc_2.g = resistance

    t_vec = h.Vector()
    soma_v_1 = h.Vector()
    soma_v_2 = h.Vector()
    t_vec.record(h._ref_t)
    soma_v_1.record(self.cell1.soma[0](0.5)._ref_v)
    soma_v_2.record(self.cell2.soma[0](0.5)._ref_v)

    self.t_vec = t_vec
    self.soma_v_1 = soma_v_1
    self.soma_v_2 = soma_v_2

    h.finitialize(-70 * mV)
    h.continuerun(h.tstop * ms)

plot_model()

Plot the voltage traces of both cells to visualize gap junction coupling.

This method creates a plot showing the membrane potential of both cells over time, highlighting the effect of gap junction coupling when a current step is applied to cell 1.

Source code in bmtool/synapses.py

def plot_model(self):
    """
    Plot the voltage traces of both cells to visualize gap junction coupling.

    This method creates a plot showing the membrane potential of both cells over time,
    highlighting the effect of gap junction coupling when a current step is applied to cell 1.
    """
    t_range = [self.general_settings['tstart'] - 100., self.general_settings['tstart']+self.general_settings['tdur'] + 100.]
    t = np.array(self.t_vec)
    v1 = np.array(self.soma_v_1)
    v2 = np.array(self.soma_v_2)
    tidx = (t >= t_range[0]) & (t <= t_range[1])

    plt.figure()
    plt.plot(t[tidx], v1[tidx], 'b', label=f'{self.cell_name} 1')
    plt.plot(t[tidx], v2[tidx], 'r', label=f'{self.cell_name} 2')
    plt.title(f"{self.cell_name} gap junction")
    plt.xlabel('Time (ms)')
    plt.ylabel('Membrane Voltage (mV)')
    plt.legend()
    plt.show()        

coupling_coefficient(t, v1, v2, t_start, t_end, dt=h.dt)

Calculate the coupling coefficient between two cells connected by a gap junction.

Parameters:

t : array-like Time vector. v1 : array-like Voltage trace of the cell receiving the current injection. v2 : array-like Voltage trace of the coupled cell. t_start : float Start time for calculating the steady-state voltage change. t_end : float End time for calculating the steady-state voltage change. dt : float, optional Time step of the simulation. Default is h.dt.

Returns:

float The coupling coefficient, defined as the ratio of voltage change in cell 2 to voltage change in cell 1 (ΔV₂/ΔV₁).

Source code in bmtool/synapses.py

def coupling_coefficient(self,t, v1, v2, t_start, t_end, dt=h.dt):
    """
    Calculate the coupling coefficient between two cells connected by a gap junction.

    Parameters:
    -----------
    t : array-like
        Time vector.
    v1 : array-like
        Voltage trace of the cell receiving the current injection.
    v2 : array-like
        Voltage trace of the coupled cell.
    t_start : float
        Start time for calculating the steady-state voltage change.
    t_end : float
        End time for calculating the steady-state voltage change.
    dt : float, optional
        Time step of the simulation. Default is h.dt.

    Returns:
    --------
    float
        The coupling coefficient, defined as the ratio of voltage change in cell 2 
        to voltage change in cell 1 (ΔV₂/ΔV₁).
    """
    t = np.asarray(t)
    v1 = np.asarray(v1)
    v2 = np.asarray(v2)
    idx1 = np.nonzero(t < t_start)[0][-1]
    idx2 = np.nonzero(t < t_end)[0][-1]
    return (v2[idx2] - v2[idx1]) / (v1[idx2] - v1[idx1])

InteractiveTuner()

Source code in bmtool/synapses.py

    def InteractiveTuner(self):
        w_run = widgets.Button(description='Run', icon='history', button_style='primary')
        values = [i * 10**-4 for i in range(1, 101)]  # From 1e-4 to 1e-2

        # Create the SelectionSlider widget with appropriate formatting
        resistance = widgets.SelectionSlider(
            options=[("%g"%i,i) for i in values],  # Use scientific notation for display
            value=10**-3,  # Default value
            description='Resistance: ',
            continuous_update=True
)

        ui = VBox([w_run,resistance])
        display(ui)
        def on_button(*args):
            clear_output()
            display(ui)
            resistance_for_gap = resistance.value
            self.model(resistance_for_gap)
            self.plot_model()
            cc = self.coupling_coefficient(self.t_vec, self.soma_v_1, self.soma_v_2, 500, 1000)
            print(f"coupling_coefficient is {cc:0.4f}")

        on_button()    
        w_run.on_click(on_button)

Optimization Results

bmtool.synapses.SynapseOptimizationResult dataclass

Container for synaptic parameter optimization results

Source code in bmtool/synapses.py

@dataclass
class SynapseOptimizationResult:
    """Container for synaptic parameter optimization results"""
    optimal_params: Dict[str, float]
    achieved_metrics: Dict[str, float]
    target_metrics: Dict[str, float]
    error: float
    optimization_path: List[Dict[str, float]]

Synapse Optimization

bmtool.synapses.SynapseOptimizer

Source code in bmtool/synapses.py

class SynapseOptimizer:
    def __init__(self, tuner):
        """
        Initialize the synapse optimizer with parameter scaling

        Parameters:
        -----------
        tuner : SynapseTuner
            Instance of the SynapseTuner class
        """
        self.tuner = tuner
        self.optimization_history = []
        self.param_scales = {}

    def _normalize_params(self, params: np.ndarray, param_names: List[str]) -> np.ndarray:
        """
        Normalize parameters to similar scales for better optimization performance.

        Parameters:
        -----------
        params : np.ndarray
            Original parameter values.
        param_names : List[str]
            Names of the parameters corresponding to the values.

        Returns:
        --------
        np.ndarray
            Normalized parameter values.
        """
        return np.array([params[i] / self.param_scales[name] for i, name in enumerate(param_names)])

    def _denormalize_params(self, normalized_params: np.ndarray, param_names: List[str]) -> np.ndarray:
        """
        Convert normalized parameters back to original scale.

        Parameters:
        -----------
        normalized_params : np.ndarray
            Normalized parameter values.
        param_names : List[str]
            Names of the parameters corresponding to the normalized values.

        Returns:
        --------
        np.ndarray
            Denormalized parameter values in their original scale.
        """
        return np.array([normalized_params[i] * self.param_scales[name] for i, name in enumerate(param_names)])

    def _calculate_metrics(self) -> Dict[str, float]:
        """
        Calculate standard metrics from the current simulation.

        This method runs either a single event simulation, a train input simulation, 
        or both based on configuration flags, and calculates relevant synaptic metrics.

        Returns:
        --------
        Dict[str, float]
            Dictionary of calculated metrics including:
            - induction: measure of synaptic facilitation/depression
            - ppr: paired-pulse ratio
            - recovery: recovery from facilitation/depression
            - max_amplitude: maximum synaptic response amplitude
            - rise_time: time for synaptic response to rise from 20% to 80% of peak
            - decay_time: time constant of synaptic response decay
        """
        # Set these to 0 for when we return the dict 
        induction = 0
        ppr = 0
        recovery = 0
        amp = 0
        rise_time = 0
        decay_time = 0

        if self.run_single_event:
            self.tuner.SingleEvent(plot_and_print=False)
            rise_time = self.tuner.rise_time
            decay_time = self.tuner.decay_time

        if self.run_train_input:
            self.tuner._simulate_model(self.train_frequency, self.train_delay)
            amp = self.tuner._response_amplitude()
            ppr, induction, recovery = self.tuner._calc_ppr_induction_recovery(amp, print_math=False)
            amp = self.tuner._find_max_amp(amp)

        return {
            'induction': float(induction),
            'ppr': float(ppr),
            'recovery': float(recovery),
            'max_amplitude': float(amp),
            'rise_time': float(rise_time),
            'decay_time': float(decay_time)
        }

    def _default_cost_function(self, metrics: Dict[str, float], target_metrics: Dict[str, float]) -> float:
        """
        Default cost function that minimizes the squared difference between achieved and target induction.

        Parameters:
        -----------
        metrics : Dict[str, float]
            Dictionary of calculated metrics from the current simulation.
        target_metrics : Dict[str, float]
            Dictionary of target metrics to optimize towards.

        Returns:
        --------
        float
            The squared error between achieved and target induction.
        """
        return float((metrics['induction'] - target_metrics['induction']) ** 2)

    def _objective_function(self, 
                          normalized_params: np.ndarray, 
                          param_names: List[str], 
                          cost_function: Callable,
                          target_metrics: Dict[str, float]) -> float:
        """
        Calculate error using provided cost function
        """
        # Denormalize parameters
        params = self._denormalize_params(normalized_params, param_names)

        # Set parameters
        for name, value in zip(param_names, params):
            setattr(self.tuner.syn, name, value)

        # just do this and have the SingleEvent handle it     
        if self.run_single_event:
            self.tuner.using_optimizer = True
            self.tuner.param_names = param_names
            self.tuner.params = params

        # Calculate metrics and error
        metrics = self._calculate_metrics()
        error = float(cost_function(metrics, target_metrics))  # Ensure error is scalar

        # Store history with denormalized values
        history_entry = {
            'params': dict(zip(param_names, params)),
            'metrics': metrics,
            'error': error
        }
        self.optimization_history.append(history_entry)

        return error

    def optimize_parameters(self, target_metrics: Dict[str, float],
                            param_bounds: Dict[str, Tuple[float, float]],
                            run_single_event:bool = False, run_train_input:bool = True,
                            train_frequency: float = 50,train_delay: float = 250,
                            cost_function: Optional[Callable] = None,
                            method: str = 'SLSQP',init_guess='random') -> SynapseOptimizationResult:
        """
        Optimize synaptic parameters to achieve target metrics.

        Parameters:
        -----------
        target_metrics : Dict[str, float]
            Target values for synaptic metrics (e.g., {'induction': 0.2, 'rise_time': 0.5})
        param_bounds : Dict[str, Tuple[float, float]]
            Bounds for each parameter to optimize (e.g., {'tau_d': (5, 50), 'Use': (0.1, 0.9)})
        run_single_event : bool, optional
            Whether to run single event simulations during optimization (default: False)
        run_train_input : bool, optional
            Whether to run train input simulations during optimization (default: True)
        train_frequency : float, optional
            Frequency of the stimulus train in Hz (default: 50)
        train_delay : float, optional
            Delay between pulse trains in ms (default: 250)
        cost_function : Optional[Callable]
            Custom cost function for optimization. If None, uses default cost function
            that optimizes induction.
        method : str, optional
            Optimization method to use (default: 'SLSQP')
        init_guess : str, optional
            Method for initial parameter guess ('random' or 'middle_guess')

        Returns:
        --------
        SynapseOptimizationResult
            Results of the optimization including optimal parameters, achieved metrics,
            target metrics, final error, and optimization path.

        Notes:
        ------
        This function uses scipy.optimize.minimize to find the optimal parameter values
        that minimize the difference between achieved and target metrics.
        """
        self.optimization_history = []
        self.train_frequency = train_frequency
        self.train_delay = train_delay
        self.run_single_event = run_single_event
        self.run_train_input = run_train_input

        param_names = list(param_bounds.keys())
        bounds = [param_bounds[name] for name in param_names]

        if cost_function is None:
            cost_function = self._default_cost_function

        # Calculate scaling factors
        self.param_scales = {
            name: max(abs(bounds[i][0]), abs(bounds[i][1]))
            for i, name in enumerate(param_names)
        }

        # Normalize bounds
        normalized_bounds = [
            (b[0]/self.param_scales[name], b[1]/self.param_scales[name])
            for name, b in zip(param_names, bounds)
        ]

        # picks with method of init value we want to use
        if init_guess=='random':
            x0 = np.array([np.random.uniform(b[0], b[1]) for b in bounds])
        elif init_guess=='middle_guess':
            x0 = [(b[0] + b[1])/2 for b in bounds]
        else:
            raise Exception("Pick a vaid init guess method either random or midde_guess")
        normalized_x0 = self._normalize_params(np.array(x0), param_names)


        # Run optimization
        result = minimize(
            self._objective_function,
            normalized_x0,
            args=(param_names, cost_function, target_metrics),
            method=method,
            bounds=normalized_bounds
        )

        # Get final parameters and metrics
        final_params = dict(zip(param_names, self._denormalize_params(result.x, param_names)))
        for name, value in final_params.items():
            setattr(self.tuner.syn, name, value)
        final_metrics = self._calculate_metrics()

        return SynapseOptimizationResult(
            optimal_params=final_params,
            achieved_metrics=final_metrics,
            target_metrics=target_metrics,
            error=result.fun,
            optimization_path=self.optimization_history
        )

    def plot_optimization_results(self, result: SynapseOptimizationResult):
        """
        Plot optimization results including convergence and final traces.

        Parameters:
        -----------
        result : SynapseOptimizationResult
            Results from optimization as returned by optimize_parameters()

        Notes:
        ------
        This method generates three plots:
        1. Error convergence plot showing how the error decreased over iterations
        2. Parameter convergence plots showing how each parameter changed
        3. Final model response with the optimal parameters

        It also prints a summary of the optimization results including target vs. achieved
        metrics and the optimal parameter values.
        """
        # Ensure errors are properly shaped for plotting
        iterations = range(len(result.optimization_path))
        errors = np.array([float(h['error']) for h in result.optimization_path]).flatten()

        # Plot error convergence
        fig1, ax1 = plt.subplots(figsize=(8, 5))
        ax1.plot(iterations, errors, label='Error')
        ax1.set_xlabel('Iteration')
        ax1.set_ylabel('Error')
        ax1.set_title('Error Convergence')
        ax1.set_yscale('log')
        ax1.legend()
        plt.tight_layout()
        plt.show()

        # Plot parameter convergence
        param_names = list(result.optimal_params.keys())
        num_params = len(param_names)
        fig2, axs = plt.subplots(nrows=num_params, ncols=1, figsize=(8, 5 * num_params))

        if num_params == 1:
            axs = [axs]

        for ax, param in zip(axs, param_names):
            values = [float(h['params'][param]) for h in result.optimization_path]
            ax.plot(iterations, values, label=f'{param}')
            ax.set_xlabel('Iteration')
            ax.set_ylabel('Parameter Value')
            ax.set_title(f'Convergence of {param}')
            ax.legend()

        plt.tight_layout()
        plt.show()

        # Print final results
        print("Optimization Results:")
        print(f"Final Error: {float(result.error):.2e}\n")
        print("Target Metrics:")
        for metric, value in result.target_metrics.items():
            achieved = result.achieved_metrics.get(metric)
            if achieved is not None and metric != 'amplitudes':  # Skip amplitude array
                print(f"{metric}: {float(achieved):.3f} (target: {float(value):.3f})")

        print("\nOptimal Parameters:")
        for param, value in result.optimal_params.items():
            print(f"{param}: {float(value):.3f}")

        # Plot final model response
        if self.run_train_input:
            self.tuner._plot_model([self.tuner.general_settings['tstart'] - self.tuner.nstim.interval / 3, self.tuner.tstop])
            amp = self.tuner._response_amplitude()
            self.tuner._calc_ppr_induction_recovery(amp)
        if self.run_single_event:
            self.tuner.ispk=None
            self.tuner.SingleEvent(plot_and_print=True)

__init__(tuner)

Initialize the synapse optimizer with parameter scaling

Parameters:

tuner : SynapseTuner Instance of the SynapseTuner class

Source code in bmtool/synapses.py

def __init__(self, tuner):
    """
    Initialize the synapse optimizer with parameter scaling

    Parameters:
    -----------
    tuner : SynapseTuner
        Instance of the SynapseTuner class
    """
    self.tuner = tuner
    self.optimization_history = []
    self.param_scales = {}

_normalize_params(params, param_names)

Normalize parameters to similar scales for better optimization performance.

Parameters:

params : np.ndarray Original parameter values. param_names : List[str] Names of the parameters corresponding to the values.

Returns:

np.ndarray Normalized parameter values.

Source code in bmtool/synapses.py

def _normalize_params(self, params: np.ndarray, param_names: List[str]) -> np.ndarray:
    """
    Normalize parameters to similar scales for better optimization performance.

    Parameters:
    -----------
    params : np.ndarray
        Original parameter values.
    param_names : List[str]
        Names of the parameters corresponding to the values.

    Returns:
    --------
    np.ndarray
        Normalized parameter values.
    """
    return np.array([params[i] / self.param_scales[name] for i, name in enumerate(param_names)])

_denormalize_params(normalized_params, param_names)

Convert normalized parameters back to original scale.

Parameters:

normalized_params : np.ndarray Normalized parameter values. param_names : List[str] Names of the parameters corresponding to the normalized values.

Returns:

np.ndarray Denormalized parameter values in their original scale.

Source code in bmtool/synapses.py

def _denormalize_params(self, normalized_params: np.ndarray, param_names: List[str]) -> np.ndarray:
    """
    Convert normalized parameters back to original scale.

    Parameters:
    -----------
    normalized_params : np.ndarray
        Normalized parameter values.
    param_names : List[str]
        Names of the parameters corresponding to the normalized values.

    Returns:
    --------
    np.ndarray
        Denormalized parameter values in their original scale.
    """
    return np.array([normalized_params[i] * self.param_scales[name] for i, name in enumerate(param_names)])

_calculate_metrics()

Calculate standard metrics from the current simulation.

This method runs either a single event simulation, a train input simulation, or both based on configuration flags, and calculates relevant synaptic metrics.

Returns:

Dict[str, float] Dictionary of calculated metrics including: - induction: measure of synaptic facilitation/depression - ppr: paired-pulse ratio - recovery: recovery from facilitation/depression - max_amplitude: maximum synaptic response amplitude - rise_time: time for synaptic response to rise from 20% to 80% of peak - decay_time: time constant of synaptic response decay

Source code in bmtool/synapses.py

def _calculate_metrics(self) -> Dict[str, float]:
    """
    Calculate standard metrics from the current simulation.

    This method runs either a single event simulation, a train input simulation, 
    or both based on configuration flags, and calculates relevant synaptic metrics.

    Returns:
    --------
    Dict[str, float]
        Dictionary of calculated metrics including:
        - induction: measure of synaptic facilitation/depression
        - ppr: paired-pulse ratio
        - recovery: recovery from facilitation/depression
        - max_amplitude: maximum synaptic response amplitude
        - rise_time: time for synaptic response to rise from 20% to 80% of peak
        - decay_time: time constant of synaptic response decay
    """
    # Set these to 0 for when we return the dict 
    induction = 0
    ppr = 0
    recovery = 0
    amp = 0
    rise_time = 0
    decay_time = 0

    if self.run_single_event:
        self.tuner.SingleEvent(plot_and_print=False)
        rise_time = self.tuner.rise_time
        decay_time = self.tuner.decay_time

    if self.run_train_input:
        self.tuner._simulate_model(self.train_frequency, self.train_delay)
        amp = self.tuner._response_amplitude()
        ppr, induction, recovery = self.tuner._calc_ppr_induction_recovery(amp, print_math=False)
        amp = self.tuner._find_max_amp(amp)

    return {
        'induction': float(induction),
        'ppr': float(ppr),
        'recovery': float(recovery),
        'max_amplitude': float(amp),
        'rise_time': float(rise_time),
        'decay_time': float(decay_time)
    }

_default_cost_function(metrics, target_metrics)

Default cost function that minimizes the squared difference between achieved and target induction.

Parameters:

metrics : Dict[str, float] Dictionary of calculated metrics from the current simulation. target_metrics : Dict[str, float] Dictionary of target metrics to optimize towards.

Returns:

float The squared error between achieved and target induction.

Source code in bmtool/synapses.py

def _default_cost_function(self, metrics: Dict[str, float], target_metrics: Dict[str, float]) -> float:
    """
    Default cost function that minimizes the squared difference between achieved and target induction.

    Parameters:
    -----------
    metrics : Dict[str, float]
        Dictionary of calculated metrics from the current simulation.
    target_metrics : Dict[str, float]
        Dictionary of target metrics to optimize towards.

    Returns:
    --------
    float
        The squared error between achieved and target induction.
    """
    return float((metrics['induction'] - target_metrics['induction']) ** 2)

_objective_function(normalized_params, param_names, cost_function, target_metrics)

Calculate error using provided cost function

Source code in bmtool/synapses.py

def _objective_function(self, 
                      normalized_params: np.ndarray, 
                      param_names: List[str], 
                      cost_function: Callable,
                      target_metrics: Dict[str, float]) -> float:
    """
    Calculate error using provided cost function
    """
    # Denormalize parameters
    params = self._denormalize_params(normalized_params, param_names)

    # Set parameters
    for name, value in zip(param_names, params):
        setattr(self.tuner.syn, name, value)

    # just do this and have the SingleEvent handle it     
    if self.run_single_event:
        self.tuner.using_optimizer = True
        self.tuner.param_names = param_names
        self.tuner.params = params

    # Calculate metrics and error
    metrics = self._calculate_metrics()
    error = float(cost_function(metrics, target_metrics))  # Ensure error is scalar

    # Store history with denormalized values
    history_entry = {
        'params': dict(zip(param_names, params)),
        'metrics': metrics,
        'error': error
    }
    self.optimization_history.append(history_entry)

    return error

optimize_parameters(target_metrics, param_bounds, run_single_event=False, run_train_input=True, train_frequency=50, train_delay=250, cost_function=None, method='SLSQP', init_guess='random')

Optimize synaptic parameters to achieve target metrics.

Parameters:

target_metrics : Dict[str, float] Target values for synaptic metrics (e.g., {'induction': 0.2, 'rise_time': 0.5}) param_bounds : Dict[str, Tuple[float, float]] Bounds for each parameter to optimize (e.g., {'tau_d': (5, 50), 'Use': (0.1, 0.9)}) run_single_event : bool, optional Whether to run single event simulations during optimization (default: False) run_train_input : bool, optional Whether to run train input simulations during optimization (default: True) train_frequency : float, optional Frequency of the stimulus train in Hz (default: 50) train_delay : float, optional Delay between pulse trains in ms (default: 250) cost_function : Optional[Callable] Custom cost function for optimization. If None, uses default cost function that optimizes induction. method : str, optional Optimization method to use (default: 'SLSQP') init_guess : str, optional Method for initial parameter guess ('random' or 'middle_guess')

Returns:

SynapseOptimizationResult Results of the optimization including optimal parameters, achieved metrics, target metrics, final error, and optimization path.

Notes:

This function uses scipy.optimize.minimize to find the optimal parameter values that minimize the difference between achieved and target metrics.

Source code in bmtool/synapses.py

def optimize_parameters(self, target_metrics: Dict[str, float],
                        param_bounds: Dict[str, Tuple[float, float]],
                        run_single_event:bool = False, run_train_input:bool = True,
                        train_frequency: float = 50,train_delay: float = 250,
                        cost_function: Optional[Callable] = None,
                        method: str = 'SLSQP',init_guess='random') -> SynapseOptimizationResult:
    """
    Optimize synaptic parameters to achieve target metrics.

    Parameters:
    -----------
    target_metrics : Dict[str, float]
        Target values for synaptic metrics (e.g., {'induction': 0.2, 'rise_time': 0.5})
    param_bounds : Dict[str, Tuple[float, float]]
        Bounds for each parameter to optimize (e.g., {'tau_d': (5, 50), 'Use': (0.1, 0.9)})
    run_single_event : bool, optional
        Whether to run single event simulations during optimization (default: False)
    run_train_input : bool, optional
        Whether to run train input simulations during optimization (default: True)
    train_frequency : float, optional
        Frequency of the stimulus train in Hz (default: 50)
    train_delay : float, optional
        Delay between pulse trains in ms (default: 250)
    cost_function : Optional[Callable]
        Custom cost function for optimization. If None, uses default cost function
        that optimizes induction.
    method : str, optional
        Optimization method to use (default: 'SLSQP')
    init_guess : str, optional
        Method for initial parameter guess ('random' or 'middle_guess')

    Returns:
    --------
    SynapseOptimizationResult
        Results of the optimization including optimal parameters, achieved metrics,
        target metrics, final error, and optimization path.

    Notes:
    ------
    This function uses scipy.optimize.minimize to find the optimal parameter values
    that minimize the difference between achieved and target metrics.
    """
    self.optimization_history = []
    self.train_frequency = train_frequency
    self.train_delay = train_delay
    self.run_single_event = run_single_event
    self.run_train_input = run_train_input

    param_names = list(param_bounds.keys())
    bounds = [param_bounds[name] for name in param_names]

    if cost_function is None:
        cost_function = self._default_cost_function

    # Calculate scaling factors
    self.param_scales = {
        name: max(abs(bounds[i][0]), abs(bounds[i][1]))
        for i, name in enumerate(param_names)
    }

    # Normalize bounds
    normalized_bounds = [
        (b[0]/self.param_scales[name], b[1]/self.param_scales[name])
        for name, b in zip(param_names, bounds)
    ]

    # picks with method of init value we want to use
    if init_guess=='random':
        x0 = np.array([np.random.uniform(b[0], b[1]) for b in bounds])
    elif init_guess=='middle_guess':
        x0 = [(b[0] + b[1])/2 for b in bounds]
    else:
        raise Exception("Pick a vaid init guess method either random or midde_guess")
    normalized_x0 = self._normalize_params(np.array(x0), param_names)


    # Run optimization
    result = minimize(
        self._objective_function,
        normalized_x0,
        args=(param_names, cost_function, target_metrics),
        method=method,
        bounds=normalized_bounds
    )

    # Get final parameters and metrics
    final_params = dict(zip(param_names, self._denormalize_params(result.x, param_names)))
    for name, value in final_params.items():
        setattr(self.tuner.syn, name, value)
    final_metrics = self._calculate_metrics()

    return SynapseOptimizationResult(
        optimal_params=final_params,
        achieved_metrics=final_metrics,
        target_metrics=target_metrics,
        error=result.fun,
        optimization_path=self.optimization_history
    )

plot_optimization_results(result)

Plot optimization results including convergence and final traces.

Parameters:

result : SynapseOptimizationResult Results from optimization as returned by optimize_parameters()

Notes:

This method generates three plots: 1. Error convergence plot showing how the error decreased over iterations 2. Parameter convergence plots showing how each parameter changed 3. Final model response with the optimal parameters

It also prints a summary of the optimization results including target vs. achieved metrics and the optimal parameter values.

Source code in bmtool/synapses.py

def plot_optimization_results(self, result: SynapseOptimizationResult):
    """
    Plot optimization results including convergence and final traces.

    Parameters:
    -----------
    result : SynapseOptimizationResult
        Results from optimization as returned by optimize_parameters()

    Notes:
    ------
    This method generates three plots:
    1. Error convergence plot showing how the error decreased over iterations
    2. Parameter convergence plots showing how each parameter changed
    3. Final model response with the optimal parameters

    It also prints a summary of the optimization results including target vs. achieved
    metrics and the optimal parameter values.
    """
    # Ensure errors are properly shaped for plotting
    iterations = range(len(result.optimization_path))
    errors = np.array([float(h['error']) for h in result.optimization_path]).flatten()

    # Plot error convergence
    fig1, ax1 = plt.subplots(figsize=(8, 5))
    ax1.plot(iterations, errors, label='Error')
    ax1.set_xlabel('Iteration')
    ax1.set_ylabel('Error')
    ax1.set_title('Error Convergence')
    ax1.set_yscale('log')
    ax1.legend()
    plt.tight_layout()
    plt.show()

    # Plot parameter convergence
    param_names = list(result.optimal_params.keys())
    num_params = len(param_names)
    fig2, axs = plt.subplots(nrows=num_params, ncols=1, figsize=(8, 5 * num_params))

    if num_params == 1:
        axs = [axs]

    for ax, param in zip(axs, param_names):
        values = [float(h['params'][param]) for h in result.optimization_path]
        ax.plot(iterations, values, label=f'{param}')
        ax.set_xlabel('Iteration')
        ax.set_ylabel('Parameter Value')
        ax.set_title(f'Convergence of {param}')
        ax.legend()

    plt.tight_layout()
    plt.show()

    # Print final results
    print("Optimization Results:")
    print(f"Final Error: {float(result.error):.2e}\n")
    print("Target Metrics:")
    for metric, value in result.target_metrics.items():
        achieved = result.achieved_metrics.get(metric)
        if achieved is not None and metric != 'amplitudes':  # Skip amplitude array
            print(f"{metric}: {float(achieved):.3f} (target: {float(value):.3f})")

    print("\nOptimal Parameters:")
    for param, value in result.optimal_params.items():
        print(f"{param}: {float(value):.3f}")

    # Plot final model response
    if self.run_train_input:
        self.tuner._plot_model([self.tuner.general_settings['tstart'] - self.tuner.nstim.interval / 3, self.tuner.tstop])
        amp = self.tuner._response_amplitude()
        self.tuner._calc_ppr_induction_recovery(amp)
    if self.run_single_event:
        self.tuner.ispk=None
        self.tuner.SingleEvent(plot_and_print=True)

Gap Junction Optimization Results

bmtool.synapses.GapOptimizationResult dataclass

Container for gap junction optimization results

Source code in bmtool/synapses.py

@dataclass
class GapOptimizationResult:
    """Container for gap junction optimization results"""
    optimal_resistance: float
    achieved_cc: float
    target_cc: float
    error: float
    optimization_path: List[Dict[str, float]]

Gap Junction Optimization

bmtool.synapses.GapJunctionOptimizer

Source code in bmtool/synapses.py

class GapJunctionOptimizer:
    def __init__(self, tuner):
        """
        Initialize the gap junction optimizer

        Parameters:
        -----------
        tuner : GapJunctionTuner
            Instance of the GapJunctionTuner class
        """
        self.tuner = tuner
        self.optimization_history = []

    def _objective_function(self, resistance: float, target_cc: float) -> float:
        """
        Calculate error between achieved and target coupling coefficient

        Parameters:
        -----------
        resistance : float
            Gap junction resistance to try
        target_cc : float
            Target coupling coefficient to match

        Returns:
        --------
        float : Error between achieved and target coupling coefficient
        """
        # Run model with current resistance
        self.tuner.model(resistance)

        # Calculate coupling coefficient
        achieved_cc = self.tuner.coupling_coefficient(
            self.tuner.t_vec, 
            self.tuner.soma_v_1, 
            self.tuner.soma_v_2,
            self.tuner.general_settings['tstart'],
            self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur']
        )

        # Calculate error
        error = (achieved_cc - target_cc) ** 2 #MSE

        # Store history
        self.optimization_history.append({
            'resistance': resistance,
            'achieved_cc': achieved_cc,
            'error': error
        })

        return error

    def optimize_resistance(self, target_cc: float, 
                          resistance_bounds: tuple = (1e-4, 1e-2),
                          method: str = 'bounded') -> GapOptimizationResult:
        """
        Optimize gap junction resistance to achieve a target coupling coefficient.

        Parameters:
        -----------
        target_cc : float
            Target coupling coefficient to achieve (between 0 and 1)
        resistance_bounds : tuple, optional
            (min, max) bounds for resistance search in MOhm. Default is (1e-4, 1e-2).
        method : str, optional
            Optimization method to use. Default is 'bounded' which works well
            for single-parameter optimization.

        Returns:
        --------
        GapOptimizationResult
            Container with optimization results including:
            - optimal_resistance: The optimized resistance value
            - achieved_cc: The coupling coefficient achieved with the optimal resistance
            - target_cc: The target coupling coefficient
            - error: The final error (squared difference between target and achieved)
            - optimization_path: List of all values tried during optimization

        Notes:
        ------
        Uses scipy.optimize.minimize_scalar with bounded method, which is
        appropriate for this single-parameter optimization problem.
        """
        self.optimization_history = []

        # Run optimization
        result = minimize_scalar(
            self._objective_function,
            args=(target_cc,),
            bounds=resistance_bounds,
            method=method
        )

        # Run final model with optimal resistance
        self.tuner.model(result.x)
        final_cc = self.tuner.coupling_coefficient(
            self.tuner.t_vec,
            self.tuner.soma_v_1,
            self.tuner.soma_v_2,
            self.tuner.general_settings['tstart'],
            self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur']
        )

        # Package up our results
        optimization_result = GapOptimizationResult(
            optimal_resistance=result.x,
            achieved_cc=final_cc,
            target_cc=target_cc,
            error=result.fun,
            optimization_path=self.optimization_history
        )

        return optimization_result

    def plot_optimization_results(self, result: GapOptimizationResult):
        """
        Plot optimization results including convergence and final voltage traces

        Parameters:
        -----------
        result : GapOptimizationResult
            Results from optimization
        """
        fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))

        # Plot voltage traces
        t_range = [
            self.tuner.general_settings['tstart'] - 100.,
            self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur'] + 100.
        ]
        t = np.array(self.tuner.t_vec)
        v1 = np.array(self.tuner.soma_v_1)
        v2 = np.array(self.tuner.soma_v_2)
        tidx = (t >= t_range[0]) & (t <= t_range[1])

        ax1.plot(t[tidx], v1[tidx], 'b', label=f'{self.tuner.cell_name} 1')
        ax1.plot(t[tidx], v2[tidx], 'r', label=f'{self.tuner.cell_name} 2')
        ax1.set_xlabel('Time (ms)')
        ax1.set_ylabel('Membrane Voltage (mV)')
        ax1.legend()
        ax1.set_title('Optimized Voltage Traces')

        # Plot error convergence
        errors = [h['error'] for h in result.optimization_path]
        ax2.plot(errors)
        ax2.set_xlabel('Iteration')
        ax2.set_ylabel('Error')
        ax2.set_title('Error Convergence')
        ax2.set_yscale('log')

        # Plot resistance convergence
        resistances = [h['resistance'] for h in result.optimization_path]
        ax3.plot(resistances)
        ax3.set_xlabel('Iteration')
        ax3.set_ylabel('Resistance')
        ax3.set_title('Resistance Convergence')
        ax3.set_yscale('log')

        # Print final results
        result_text = (
            f'Optimal Resistance: {result.optimal_resistance:.2e}\n'
            f'Target CC: {result.target_cc:.3f}\n'
            f'Achieved CC: {result.achieved_cc:.3f}\n'
            f'Final Error: {result.error:.2e}'
        )
        ax4.text(0.1, 0.7, result_text, transform=ax4.transAxes, fontsize=10)
        ax4.axis('off')

        plt.tight_layout()
        plt.show()

    def parameter_sweep(self, resistance_range: np.ndarray) -> dict:
        """
        Perform a parameter sweep across different resistance values.

        Parameters:
        -----------
        resistance_range : np.ndarray
            Array of resistance values to test.

        Returns:
        --------
        dict
            Dictionary containing the results of the parameter sweep, with keys:
            - 'resistance': List of resistance values tested
            - 'coupling_coefficient': Corresponding coupling coefficients

        Notes:
        ------
        This method is useful for understanding the relationship between gap junction
        resistance and coupling coefficient before attempting optimization.
        """
        results = {
            'resistance': [],
            'coupling_coefficient': []
        }

        for resistance in tqdm(resistance_range, desc="Sweeping resistance values"):
            self.tuner.model(resistance)
            cc = self.tuner.coupling_coefficient(
                self.tuner.t_vec,
                self.tuner.soma_v_1,
                self.tuner.soma_v_2,
                self.tuner.general_settings['tstart'],
                self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur']
            )

            results['resistance'].append(resistance)
            results['coupling_coefficient'].append(cc)

        return results

__init__(tuner)

Initialize the gap junction optimizer

Parameters:

tuner : GapJunctionTuner Instance of the GapJunctionTuner class

Source code in bmtool/synapses.py

def __init__(self, tuner):
    """
    Initialize the gap junction optimizer

    Parameters:
    -----------
    tuner : GapJunctionTuner
        Instance of the GapJunctionTuner class
    """
    self.tuner = tuner
    self.optimization_history = []

_objective_function(resistance, target_cc)

Calculate error between achieved and target coupling coefficient

Parameters:

resistance : float Gap junction resistance to try target_cc : float Target coupling coefficient to match

Returns:

float : Error between achieved and target coupling coefficient

Source code in bmtool/synapses.py

def _objective_function(self, resistance: float, target_cc: float) -> float:
    """
    Calculate error between achieved and target coupling coefficient

    Parameters:
    -----------
    resistance : float
        Gap junction resistance to try
    target_cc : float
        Target coupling coefficient to match

    Returns:
    --------
    float : Error between achieved and target coupling coefficient
    """
    # Run model with current resistance
    self.tuner.model(resistance)

    # Calculate coupling coefficient
    achieved_cc = self.tuner.coupling_coefficient(
        self.tuner.t_vec, 
        self.tuner.soma_v_1, 
        self.tuner.soma_v_2,
        self.tuner.general_settings['tstart'],
        self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur']
    )

    # Calculate error
    error = (achieved_cc - target_cc) ** 2 #MSE

    # Store history
    self.optimization_history.append({
        'resistance': resistance,
        'achieved_cc': achieved_cc,
        'error': error
    })

    return error

optimize_resistance(target_cc, resistance_bounds=(0.0001, 0.01), method='bounded')

Optimize gap junction resistance to achieve a target coupling coefficient.

Parameters:

target_cc : float Target coupling coefficient to achieve (between 0 and 1) resistance_bounds : tuple, optional (min, max) bounds for resistance search in MOhm. Default is (1e-4, 1e-2). method : str, optional Optimization method to use. Default is 'bounded' which works well for single-parameter optimization.

Returns:

GapOptimizationResult Container with optimization results including: - optimal_resistance: The optimized resistance value - achieved_cc: The coupling coefficient achieved with the optimal resistance - target_cc: The target coupling coefficient - error: The final error (squared difference between target and achieved) - optimization_path: List of all values tried during optimization

Notes:

Uses scipy.optimize.minimize_scalar with bounded method, which is appropriate for this single-parameter optimization problem.

Source code in bmtool/synapses.py

def optimize_resistance(self, target_cc: float, 
                      resistance_bounds: tuple = (1e-4, 1e-2),
                      method: str = 'bounded') -> GapOptimizationResult:
    """
    Optimize gap junction resistance to achieve a target coupling coefficient.

    Parameters:
    -----------
    target_cc : float
        Target coupling coefficient to achieve (between 0 and 1)
    resistance_bounds : tuple, optional
        (min, max) bounds for resistance search in MOhm. Default is (1e-4, 1e-2).
    method : str, optional
        Optimization method to use. Default is 'bounded' which works well
        for single-parameter optimization.

    Returns:
    --------
    GapOptimizationResult
        Container with optimization results including:
        - optimal_resistance: The optimized resistance value
        - achieved_cc: The coupling coefficient achieved with the optimal resistance
        - target_cc: The target coupling coefficient
        - error: The final error (squared difference between target and achieved)
        - optimization_path: List of all values tried during optimization

    Notes:
    ------
    Uses scipy.optimize.minimize_scalar with bounded method, which is
    appropriate for this single-parameter optimization problem.
    """
    self.optimization_history = []

    # Run optimization
    result = minimize_scalar(
        self._objective_function,
        args=(target_cc,),
        bounds=resistance_bounds,
        method=method
    )

    # Run final model with optimal resistance
    self.tuner.model(result.x)
    final_cc = self.tuner.coupling_coefficient(
        self.tuner.t_vec,
        self.tuner.soma_v_1,
        self.tuner.soma_v_2,
        self.tuner.general_settings['tstart'],
        self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur']
    )

    # Package up our results
    optimization_result = GapOptimizationResult(
        optimal_resistance=result.x,
        achieved_cc=final_cc,
        target_cc=target_cc,
        error=result.fun,
        optimization_path=self.optimization_history
    )

    return optimization_result

plot_optimization_results(result)

Plot optimization results including convergence and final voltage traces

Parameters:

result : GapOptimizationResult Results from optimization

Source code in bmtool/synapses.py

def plot_optimization_results(self, result: GapOptimizationResult):
    """
    Plot optimization results including convergence and final voltage traces

    Parameters:
    -----------
    result : GapOptimizationResult
        Results from optimization
    """
    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))

    # Plot voltage traces
    t_range = [
        self.tuner.general_settings['tstart'] - 100.,
        self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur'] + 100.
    ]
    t = np.array(self.tuner.t_vec)
    v1 = np.array(self.tuner.soma_v_1)
    v2 = np.array(self.tuner.soma_v_2)
    tidx = (t >= t_range[0]) & (t <= t_range[1])

    ax1.plot(t[tidx], v1[tidx], 'b', label=f'{self.tuner.cell_name} 1')
    ax1.plot(t[tidx], v2[tidx], 'r', label=f'{self.tuner.cell_name} 2')
    ax1.set_xlabel('Time (ms)')
    ax1.set_ylabel('Membrane Voltage (mV)')
    ax1.legend()
    ax1.set_title('Optimized Voltage Traces')

    # Plot error convergence
    errors = [h['error'] for h in result.optimization_path]
    ax2.plot(errors)
    ax2.set_xlabel('Iteration')
    ax2.set_ylabel('Error')
    ax2.set_title('Error Convergence')
    ax2.set_yscale('log')

    # Plot resistance convergence
    resistances = [h['resistance'] for h in result.optimization_path]
    ax3.plot(resistances)
    ax3.set_xlabel('Iteration')
    ax3.set_ylabel('Resistance')
    ax3.set_title('Resistance Convergence')
    ax3.set_yscale('log')

    # Print final results
    result_text = (
        f'Optimal Resistance: {result.optimal_resistance:.2e}\n'
        f'Target CC: {result.target_cc:.3f}\n'
        f'Achieved CC: {result.achieved_cc:.3f}\n'
        f'Final Error: {result.error:.2e}'
    )
    ax4.text(0.1, 0.7, result_text, transform=ax4.transAxes, fontsize=10)
    ax4.axis('off')

    plt.tight_layout()
    plt.show()

parameter_sweep(resistance_range)

Perform a parameter sweep across different resistance values.

Parameters:

resistance_range : np.ndarray Array of resistance values to test.

Returns:

dict Dictionary containing the results of the parameter sweep, with keys: - 'resistance': List of resistance values tested - 'coupling_coefficient': Corresponding coupling coefficients

Notes:

This method is useful for understanding the relationship between gap junction resistance and coupling coefficient before attempting optimization.

Source code in bmtool/synapses.py

def parameter_sweep(self, resistance_range: np.ndarray) -> dict:
    """
    Perform a parameter sweep across different resistance values.

    Parameters:
    -----------
    resistance_range : np.ndarray
        Array of resistance values to test.

    Returns:
    --------
    dict
        Dictionary containing the results of the parameter sweep, with keys:
        - 'resistance': List of resistance values tested
        - 'coupling_coefficient': Corresponding coupling coefficients

    Notes:
    ------
    This method is useful for understanding the relationship between gap junction
    resistance and coupling coefficient before attempting optimization.
    """
    results = {
        'resistance': [],
        'coupling_coefficient': []
    }

    for resistance in tqdm(resistance_range, desc="Sweeping resistance values"):
        self.tuner.model(resistance)
        cc = self.tuner.coupling_coefficient(
            self.tuner.t_vec,
            self.tuner.soma_v_1,
            self.tuner.soma_v_2,
            self.tuner.general_settings['tstart'],
            self.tuner.general_settings['tstart'] + self.tuner.general_settings['tdur']
        )

        results['resistance'].append(resistance)
        results['coupling_coefficient'].append(cc)

    return results