examples/example_13_post_processing/main.py
def run_neuron(traj):
    """Runs a simulation of a model neuron.

    :param traj:

        Container with all parameters.

    :return:

        An estimate of the firing rate of the neuron

    """

    # Extract all parameters from `traj`
    V_init = traj.par.neuron.V_init
    I = traj.par.neuron.I
    tau_V = traj.par.neuron.tau_V
    tau_ref = traj.par.neuron.tau_ref
    dt = traj.par.simulation.dt
    duration = traj.par.simulation.duration

    steps = int(duration / float(dt))
    # Create some containers for the Euler integration
    V_array = np.zeros(steps)
    V_array[0] = V_init
    spiketimes = []  # List to collect all times of action potentials

    # Do the Euler integration:
    print('Starting Euler Integration')
    for step in range(1, steps):
        if V_array[step-1] >= 1:
            # The membrane potential crossed the threshold and we mark this as
            # an action potential
            V_array[step] = 0
            spiketimes.append((step-1)*dt)
        elif spiketimes and step * dt - spiketimes[-1] <= tau_ref:
            # We are in the refractory period, so we simply clamp the voltage
            # to 0
            V_array[step] = 0
        else:
            # Euler Integration step:
            dV = -1/tau_V * V_array[step-1] + I
            V_array[step] = V_array[step-1] + dV*dt

    print('Finished Euler Integration')

    # Add the voltage trace and spike times
    traj.f_add_result('neuron.$', V=V_array, nspikes=len(spiketimes),
                      comment='Contains the development of the membrane potential over time '
                              'as well as the number of spikes.')
    # This result will be renamed to `traj.results.neuron.run_XXXXXXXX`.

    # And finally we return the estimate of the firing rate
    return len(spiketimes) / float(traj.par.simulation.duration) *1000
def run_neuron(traj):
    """Runs a simulation of a model neuron.

    :param traj:

        Container with all parameters.

    :return:

        An estimate of the firing rate of the neuron

    """

    # Extract all parameters from `traj`
    V_init = traj.par.neuron.V_init
    I = traj.par.neuron.I
    tau_V = traj.par.neuron.tau_V
    tau_ref = traj.par.neuron.tau_ref
    dt = traj.par.simulation.dt
    duration = traj.par.simulation.duration

    steps = int(duration / float(dt))
    # Create some containers for the Euler integration
    V_array = np.zeros(steps)
    V_array[0] = V_init
    spiketimes = []  # List to collect all times of action potentials

    # Do the Euler integration:
    print('Starting Euler Integration')
    for step in range(1, steps):
        if V_array[step-1] >= 1:
            # The membrane potential crossed the threshold and we mark this as
            # an action potential
            V_array[step] = 0
            spiketimes.append((step-1)*dt)
        elif spiketimes and step * dt - spiketimes[-1] <= tau_ref:
            # We are in the refractory period, so we simply clamp the voltage
            # to 0
            V_array[step] = 0
        else:
            # Euler Integration step:
            dV = -1/tau_V * V_array[step-1] + I
            V_array[step] = V_array[step-1] + dV*dt

    print('Finished Euler Integration')

    # Add the voltage trace and spike times
    traj.f_add_result('neuron.$', V=V_array, nspikes=len(spiketimes),
                      comment='Contains the development of the membrane potential over time '
                              'as well as the number of spikes.')
    # This result will be renamed to `traj.results.neuron.run_XXXXXXXX`.

    # And finally we return the estimate of the firing rate
    return len(spiketimes) / float(traj.par.simulation.duration) *1000
Runs a simulation of a model neuron.

    :param traj:

        Container with all parameters.

    :return:

        An estimate of the firing rate of the neuron