structured_inhibition_spatial_network_model/scripts/spatial_network/perlin_map/run_simulation_perlin_map.py

import numpy as np
from brian2.units import *
from pypet import Environment, cartesian_product
from pypet.brian2.parameter import Brian2MonitorResult

from scripts.spatial_maps.spatial_network_layout import create_grid_of_excitatory_neurons, \
    create_interneuron_sheet_entropy_max_orientation, get_excitatory_neurons_in_inhibitory_axonal_clouds
from scripts.spatial_maps.perlin_map.uniform_perlin_map import UniformPerlinMap
from scripts.spatial_network.spatial_network_setup import get_synaptic_weights, \
    create_head_direction_input, ex_in_network
from scripts.spatial_network.models import *

POLARIZED = 'ellipsoid'
CIRCULAR = 'circular'
NO_SYNAPSES = 'no conn'
DATA_FOLDER = "../../../data/"
LOG_FOLDER = "../../../logs/"
TRAJ_NAME = "spatial_network_perlin_map"


def get_perlin_map(scale, seed, sheet_size, N_E):
    number_of_excitatory_neurons_per_row = int(np.sqrt(N_E))

    # TODO: The +1 should not be there
    input_map = UniformPerlinMap(number_of_excitatory_neurons_per_row + 1, number_of_excitatory_neurons_per_row + 1,
                                 scale, sheet_size, sheet_size, seed)

    return input_map.get_tuning


def get_input_head_directions(traj):
    directions = np.linspace(-np.pi, np.pi, traj.input.number_of_directions, endpoint=False)
    return directions


def spatial_network_with_entropy_maximisation(traj):
    sheet_size = traj.input_map.sheet_size

    N_E = traj.network.N_E
    N_I = traj.network.N_I

    perlin_map = get_perlin_map(traj.input_map.scale, traj.input_map.seed,
                                sheet_size, N_E)

    ex_positions, ex_tunings = create_grid_of_excitatory_neurons(sheet_size, int(np.sqrt(N_E)), perlin_map)

    inhibitory_axon_long_axis = traj.morphology.long_axis
    inhibitory_axon_short_axis = traj.morphology.short_axis

    entropy_maximisation_trial_orientations = traj.interneuron.entropy_maximisation.trial_orientations if \
        inhibitory_axon_long_axis != inhibitory_axon_short_axis else 0

    inhibitory_axonal_clouds, ellipse_single_trial_entropy = create_interneuron_sheet_entropy_max_orientation(
        ex_positions, ex_tunings, N_I, inhibitory_axon_long_axis,
        inhibitory_axon_short_axis, sheet_size,
        sheet_size, trial_orientations=entropy_maximisation_trial_orientations)

    ie_connections = get_excitatory_neurons_in_inhibitory_axonal_clouds(ex_positions, inhibitory_axonal_clouds)

    inhibitory_synapse_strength = traj.synapse.inhibitory * nS
    excitatory_synapse_strength = traj.synapse.excitatory * mV

    if inhibitory_synapse_strength != 0.0 * nS and excitatory_synapse_strength != 0.0 * mV \
            and inhibitory_axon_long_axis == inhibitory_axon_short_axis:
        traj.f_add_derived_parameter("morphology.morph_label", CIRCULAR,
                                     comment="Interneuron morphology of this run is circular")
    elif inhibitory_synapse_strength != 0.0 * nS and excitatory_synapse_strength != 0.0 * mV:
        traj.f_add_derived_parameter("morphology.morph_label", POLARIZED,
                                     comment="Interneuron morphology of this run is ellipsoid")
    else:
        traj.f_add_derived_parameter("morphology.morph_label", NO_SYNAPSES,
                                     comment="There are no interneurons")

    ex_in_weights, in_ex_weights = get_synaptic_weights(N_E, N_I, ie_connections, excitatory_synapse_strength,
                                                        inhibitory_synapse_strength)

    input_directions = get_input_head_directions(traj)
    for idx, direction in enumerate(input_directions):
        # We recreate the network here for every dir, which slows down the simulation quite considerably. Otherwise,
        # we get a problem with saving and restoring the spike times (0s spike for neuron 0)

        net = ex_in_network(N_E, N_I,
                            hodgkin_huxley_eqs_with_synaptic_conductance,
                            hodgkin_huxley_params,
                            lif_interneuron_eqs,
                            lif_interneuron_params,
                            lif_interneuron_options,
                            delta_synapse_model,
                            delta_synapse_on_pre,
                            delta_synapse_param,
                            ex_in_weights,
                            exponential_synapse,
                            exponential_synapse_on_pre,
                            exponential_synapse_params,
                            in_ex_weights,
                            random_seed=traj.input_map.seed)
        input_to_excitatory_population = create_head_direction_input(traj.input.baseline * nA, ex_tunings,
                                                                     traj.input.phase_dispersion,
                                                                     traj.input.amplitude * nA, direction)

        excitatory_neurons = net["excitatory_neurons"]
        excitatory_neurons.I = input_to_excitatory_population

        inhibitory_neurons = net["interneurons"]
        inhibitory_neurons.u_ext = traj.inh_input.baseline * mV
        inhibitory_neurons.tau = traj.interneuron.tau * ms

        net.run(traj.simulation.duration * ms)

        direction_id = 'dir{:d}'.format(idx)
        traj.f_add_result(Brian2MonitorResult, '{:s}.spikes.e'.format(direction_id), net["excitatory_spike_monitor"],
                          comment='The spiketimes of the excitatory population')
        traj.f_add_result(Brian2MonitorResult, '{:s}.spikes.i'.format(direction_id), net["inhibitory_spike_monitor"],
                          comment='The spiketimes of the inhibitory population')
    traj.f_add_result('ex_positions', np.array(ex_positions),
                      comment='The positions of the excitatory neurons on the sheet')
    traj.f_add_result('ex_tunings', np.array(ex_tunings),
                      comment='The input tunings of the excitatory neurons')

    ie_connections_save_array = np.zeros((N_I, N_E))
    for i_idx, ie_conn in enumerate(ie_connections):
        for e_idx in ie_conn:
            ie_connections_save_array[i_idx, e_idx] = 1
    traj.f_add_result('ie_adjacency', ie_connections_save_array,
                      comment='Recurrent connection adjacency matrix')

    axon_cloud_save_list = [[p.x, p.y, p.phi] for p in inhibitory_axonal_clouds]
    axon_cloud_save_array = np.array(axon_cloud_save_list)
    traj.f_add_result('inhibitory_axonal_cloud_array', axon_cloud_save_array,
                      comment='The inhibitory axonal clouds')

    return 1


def main():
    # TODO: Set ncores to the desired number of processes to use by pypet
    env = Environment(trajectory=TRAJ_NAME,
                      comment="Compare the head direction tuning for circular and polarized interneuron morphology, "
                              "when tuning orientations to maximise entropy of connected excitatory tunings.",
                      multiproc=True, filename=DATA_FOLDER, ncores=3, overwrite_file=True, log_folder=LOG_FOLDER)

    traj = env.trajectory

    traj.f_add_parameter_group("input_map")
    traj.f_add_parameter("input_map.scale", 200.0, comment="Scaling factor of the input map")
    traj.f_add_parameter("input_map.seed", 1, comment="Random seed for input map generation.")
    traj.f_add_parameter("input_map.sheet_size", 900, comment="Sheet size in um")

    traj.f_add_parameter_group("network")
    traj.f_add_parameter("network.N_E", 3600, comment="Number of excitatory neurons")
    traj.f_add_parameter("network.N_I", 400, comment="Number of inhibitory neurons")

    traj.f_add_parameter_group("interneuron")
    traj.f_add_parameter("interneuron.tau", 7., comment="Interneuron timescale in ms")
    traj.f_add_parameter("interneuron.entropy_maximisation.trial_orientations", 30,
                         comment="Steps for entropy maximisation")

    traj.f_add_parameter_group("synapse")
    traj.f_add_parameter("synapse.inhibitory", 30.0, "Strength of conductance-based inhibitory synapse in nS.")
    traj.f_add_parameter("synapse.excitatory", 2.5, "Strength of conductance-based inhibitory synapse in mV.")

    traj.f_add_parameter_group("input")
    traj.f_add_parameter("input.phase_dispersion", 2.5, comment="Standard deviation of incoming head direction input.")
    traj.f_add_parameter("input.baseline", 0.05, comment="Head direction input baseline")
    traj.f_add_parameter("input.amplitude", 0.6, comment="Head direction input amplitude")
    traj.f_add_parameter("input.number_of_directions", 12, comment="Number of probed directions")

    traj.f_add_parameter_group("inh_input")
    traj.f_add_parameter("inh_input.baseline", -50., comment="Head direction input baseline")
    traj.f_add_parameter("inh_input.amplitude", 0., comment="Head direction input amplitude")

    traj.f_add_parameter_group("morphology")
    traj.f_add_parameter("morphology.long_axis", 100.0, comment="Long axis of axon ellipsoid")
    traj.f_add_parameter("morphology.short_axis", 25.0, comment="Short axis of axon ellipsoid")

    traj.f_add_parameter_group("simulation")
    traj.f_add_parameter("simulation.dt", 0.1, comment="Network simulation time step in ms")
    traj.f_add_parameter("simulation.duration", 1000, comment="Network simulation duration in ms")

    # To test if the simulation, analysis and plotting work as intended, use this setup to have a lower overall runtime

    # scale_range = [200.0]
    # seed_range = [1]

    # TODO: This is the parameter range used for the results shown in Fig 4. Uncomment for the full run.

    scale_range = np.linspace(1.0, 800.0, 24, endpoint=True).tolist()
    seed_range = range(10)

    ellipsoid_parameter_exploration = {
        "morphology.long_axis": [100.0],
        "morphology.short_axis": [25.0],
        "input_map.scale": scale_range,
        "input_map.seed": seed_range,
        "synapse.inhibitory": [30.],
        "synapse.excitatory": [2.5]
    }

    corresponding_circular_radius = float(np.sqrt(ellipsoid_parameter_exploration[
                                                      "morphology.long_axis"][0] * ellipsoid_parameter_exploration[
                                                      "morphology.short_axis"][0]))

    circle_parameter_exploration = {
        "morphology.long_axis": [corresponding_circular_radius],
        "morphology.short_axis": [corresponding_circular_radius],
        "input_map.scale": ellipsoid_parameter_exploration["input_map.scale"],
        "input_map.seed": ellipsoid_parameter_exploration["input_map.seed"],
        "synapse.inhibitory": ellipsoid_parameter_exploration["synapse.inhibitory"],
        "synapse.excitatory": ellipsoid_parameter_exploration["synapse.excitatory"]
    }

    no_conn_parameter_exploration = {
        "morphology.long_axis": [corresponding_circular_radius],
        "morphology.short_axis": [corresponding_circular_radius],
        "input_map.scale": ellipsoid_parameter_exploration["input_map.scale"],
        "input_map.seed": ellipsoid_parameter_exploration["input_map.seed"],
        "synapse.inhibitory": [0.],
        "synapse.excitatory": [0.]
    }

    expanded_dicts = [cartesian_product(dict) for dict in [ellipsoid_parameter_exploration,
                                                           circle_parameter_exploration,
                                                           no_conn_parameter_exploration]]
    final_dict = {}

    for key in expanded_dicts[0].keys():
        list_of_parameter_lists = [dict[key] for dict in expanded_dicts]
        final_dict[key] = sum(list_of_parameter_lists, [])

    traj.f_explore(final_dict)
    env.run(spatial_network_with_entropy_maximisation)

    env.disable_logging()


if __name__ == "__main__":
    main()