eigenangles/balanced_network/config.py

import itertools

# number of neurons
N = 1000
# fraction of excitatory cells
f = 0.8
# exc. strength (postsynaptic amplitude in mV)
mu = 0.1
# connection probability
epsilon = 0.1
# external rate relative to threshold rate
eta = 0.9
# type of distribution for synaptic weights
syndist = 'lognormal' # 'truncated-normal' 'lognormal' 'normal'
# variance of weight sample distributions
sigma_ex = 0.12
sigma_in = 0.1
# simulation time
simtime = 61000 #ms

###### rewiring parameters ######
shuffle_source = ['E', 'I']
shuffle_target = ['E', 'I']
shuffle_frac = 1.0

add_source = 'E'
add_target = 'E'
add_source_frac = [0.2, 10000]
# add_target_frac > p/(1-p) * add_source_frac
add_target_frac = [0.02, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0]

cluster_pop = 'E'
cluster_number = 3
cluster_size = [0.02, 0.04, 0.06, 0.08]
cluster_epsilon = [0.2, 0.3, 0.4, 0.5, 0.6]

hub_pop = 'E'
hub_size = [0.04, 0.06, 0.08, 0.10]
hub_strength = [0.2, 0.3, 0.4, 0.5]
hub_epsilon = 0.5

chain_pop = 'E'
chain_size = [0.05, 0.10, 0.15, 0.20]
chain_strength = [0.3, 0.4, 0.5, 0.6]
chain_epsilon = 0.6
chain_length = 3
connectors = [0, 30]

###### derived & fixed network parameters ######
J_ex = mu
# enforce global balance state
J_in = -f * J_ex / (1-f)

dt = 0.1  # the resolution in ms
delay = [.5, 3]  #1.5 # min/max synaptic delays in ms (uniform)

NE = int(f * N)  # number of excitatory neurons
NI = N - NE # number of inhibitory neurons

in_connection_rule = 'pairwise_bernoulli'
ex_connection_rule = 'pairwise_bernoulli'

synapse_model = 'static_synapse'
neuron_model = 'iaf_psc_delta'
tauMem = 20.0  # time constant of membrane potential in ms
theta = 20.0  # membrane threshold potential in mV
neuron_params = {"C_m": 1.0,
                 "tau_m": tauMem,
                 "t_ref": 2.0,
                 "E_L": 0.0,
                 "V_reset": 0.0,
                 "V_m": 0.0,
                 "V_th": theta}

# Driving stiumulus parameters
stimulus = "poisson_generator"
parrot_input = False
CE = epsilon * NE  # estimated number of excitatory synapses per neuron
CI = epsilon * NI  # estimated number of inhibitory synapses per neuron

if J_ex:
    nu_th = theta / (J_ex * CE * tauMem)
else:
    nu_th = theta / (CE * tauMem)

nu_ex = eta * nu_th
p_rate = 1000.0 * nu_ex * CE
p_rate_func = lambda eta: 1000 * eta*nu_th * CE

###### non-network parameters ######
# cut swinging-in time
cut_inital_time = 1000 #ms
# bin size for correlation calculation
bin_size = 2 #ms
# derived number of bins
bin_num = int((simtime - cut_inital_time) / bin_size)
# seeds for multiple runs with same specification
seed = list(range(1,101))
seed_pairs = [f'{i[0]}-{i[1]}' for i in itertools.combinations(seed,2)]