sim_rewiring_ex5.py

#!/usr/bin/env python
"""Shows effectiveness of post-spike plasticity. This time with non-disjoint input assemblies."""

import os
import time

import numpy as np

from core import core_global as core
from core.spike_monitor import SpikeMonitor
from core.voltage_monitor import VoltageMonitor
from core.weight_matrix_monitor import WeightMatrixMonitor
from layers.rewiring_connection import RewiringConnection
from models.mc_lif_group import McLifGroup
from models.poisson_pattern_group import PoissonPatternGroup
from utils import utils as utils


def main(args):
    trial = args[0]
    config = args[1]
    input_params = config["input_parameters"]
    connection_params = config["connection_parameters"]
    neuron_params = config["neuron_parameters"]

    # Directory for simulation results and log files.
    output_directory = os.path.join("results", "rewiring_ex5", str(input_params["num_assembly_neurons"]),
                                    time.strftime("%y%m%d_%H%M%S"), str(trial), "data")

    # Initialize the simulation environment.
    core.init(directory=output_directory)

    # Write config file to the output directory.
    utils.write_configuration(os.path.join(output_directory, "..", "config_rewiring_ex5.yaml"), config)

    # Set the random seed.
    core.kernel.set_master_seed(config["master_seed"])

    # Create input neurons.
    inp = PoissonPatternGroup(input_params["num_inputs"], input_params["rate"], input_params["rate_bg"],
                              params=input_params)

    # Here we randomly chose the assembly neurons.
    unique_size = input_params["assembly_size"] - input_params["shared_size"]
    assembly_neurons_idc = [list(range(i * unique_size, (i + 1) * unique_size))
                            for i in range(input_params["num_assemblies"])]
    shared_neurons = range(unique_size * input_params["num_assemblies"], input_params["num_inputs"])

    for i in range(input_params["num_assemblies"]):
        assembly_neurons_idc[i] += list(np.random.choice(shared_neurons, input_params["shared_size"],
                                                         replace=False))
    assembly_neurons_idc = np.array(assembly_neurons_idc)

    np.savetxt(os.path.join(output_directory, "assembly_neurons_idc"),
               np.sort(assembly_neurons_idc), fmt="%d")

    # Set the indices of the assembly neurons.
    inp.set_assembly_neurons_idc(assembly_neurons_idc)

    # Create the neuron.
    neuron = McLifGroup(1, neuron_params["num_branches"], neuron_params)

    # Connect input to neuron.
    conn = RewiringConnection(inp, neuron, neuron.branch.syn_current, connection_params)

    # Create some monitors which will record the simulation data.
    WeightMatrixMonitor(conn, core.kernel.fn("weights", "dat"),
                        interval=config["sampling_interval_weights"])
    SpikeMonitor(neuron, core.kernel.fn("output", "ras"))
    sm_inp = SpikeMonitor(inp, core.kernel.fn("input", "ras"))
    vm_nrn = VoltageMonitor(neuron.soma, 0, core.kernel.fn("soma", "mem"))
    vm_br = []
    for i in range(neuron_params["num_branches"]):
        vm_br.append(VoltageMonitor(neuron.branch, (i, 0), core.kernel.fn("branch", "mem", i)))

    # Now simulate the model.
    simulation_time = config["simulation_time"]
    core.kernel.run_chunk(20.0, 0, simulation_time)

    sm_inp.active = False
    vm_nrn.active = False
    for vm in vm_br:
        vm.active = False
    core.kernel.run_chunk(simulation_time - 40, 0, simulation_time)

    sm_inp.active = True
    vm_nrn.active = True
    for vm in vm_br:
        vm.active = True
    core.kernel.run_chunk(20.0, 0, simulation_time)


if __name__ == '__main__':
    import copy
    from scoop import futures

    # Load the configuration file.
    config = utils.load_configuration("config_rewiring_ex5.yaml")

    configs = []
    num_trials = 25
    for trial in range(num_trials):
        config["master_seed"] = 10 * (trial + 1)
        configs.append(copy.deepcopy(config))

    r = list(futures.map(main, [[trial, config] for trial, config in enumerate(configs)]))