adaptive_simulation_decrease.py

import nest
import numpy as np

J_parameters = np.arange(0,100,0.2)

mean_rate_string_small = ""

time_string = ""
J_string = ""

for J_s in J_parameters:

    nest.SetKernelStatus({"local_num_threads":8})

    neuron_population = 1000
    simulation_time = 1000.0

    I_e = 0.0

    dict_params = {"V_peak" : 0.0
    , "V_reset" : -70.0
    , "t_ref" : 2.0
    , "g_L" : 1.0
    , "C_m" : 250.0
    , "E_ex" : 0.0
    , "E_in" : 0.0
    , "E_L" : -70.0
    , "Delta_T" : 2.0
    , "tau_w" : 1.0
    , "a" : 1.0
    , "b" : 80.5
    , "V_th" : -55.0
    #, "tau_syn_ex" : 0.2
    , "tau_syn_in" : 2.0
    , "I_e" : 500.0
    , "w": 0.0}

    neurons = nest.Create("aeif_cond_exp", neuron_population)

    nest.SetStatus(neurons, params=dict_params)

    for neuron in neurons:
        nest.SetStatus([neuron], {"V_m": dict_params["E_L"]+(dict_params["V_th"]-dict_params["E_L"])*np.random.rand()})

    spikedetector = nest.Create("spike_detector", params={"withgid": True, "withtime": True})

    for neuron in neurons:

        nest.Connect([neuron], spikedetector)

    K = 20
    d = 1.0
    J = float(J_s)

    conn_dict = {"rule": "fixed_indegree", "indegree": K}
    syn_dict = {"delay": d, "weight": J}

    nest.Connect(neurons, neurons, conn_dict, syn_dict)

    length = np.float64(0)

    for I in range(500, -10, -10):

        I_e = float(I)

        nest.SetStatus(neurons, params={"I_e": I_e})

        nest.Simulate(simulation_time)

        dSD = nest.GetStatus(spikedetector, keys='events')[0]
        evs = dSD["senders"]
        ts = dSD["times"]

        nest.ResumeSimulation()

    nest.ResetKernel()
    nest.ResetNetwork()

    time_string += str(ts[-1]) + ", "

open_file = open("adaptive_decrease.txt", "w")
open_file.write(time_string)
open_file.close()