tau_minus=20.0,
A_plus=0.01,
A_minus=0.012),
weight_dependence=AdditiveWeightDependence(w_min=0.01, w_max=10.0))
'''
exc_targets = all_cells[pre_exc]
exc_srcs = all_cells[post_exc]
inh_srcs = all_cells[pre_inh] # = []
inh_targets = all_cells[post_inh] # = []
exc_cells = all_cells[index_exc]
inh_cells = all_cells[index_inh]
'''
top_3_hubs = all_cells[top_out[-3:][1]]
ext_stim = sim.Population(len(all_cells),
sim.SpikeSourcePoisson(rate=7.5, duration=6000.0),
label="expoisson")
rconn = 0.9
ext_conn = sim.FixedProbabilityConnector(rconn)
ext_syn = sim.StaticSynapse(weight=5.925)
connections = {}
connections['ext'] = sim.Projection(ext_stim,
all_cells,
ext_conn,
ext_syn,
receptor_type='excitatory')
exc_spike_times = [
6000 + 250,
6000 + 500,
6000 + 520,
'cm': cm,
'v_rest': v_rest,
'v_thresh': v_thresh,
'tau_syn_E': tau_syn_E,
'tau_syn_I': tau_syn_I,
'e_rev_E': e_rev_E,
'e_rev_I': e_rev_I,
'v_reset': v_reset,
'tau_refrac': tau_refrac,
'i_offset': i_offset
}
cell_type = sim.IF_cond_exp(**neuronParameters)
input = sim.Population(20, sim.SpikeSourcePoisson(rate=50.0))
output = sim.Population(25, cell_type)
rand_distr = RandomDistribution('uniform', (v_reset, v_thresh), rng=NumpyRNG(seed=85524))
output.initialize(v=rand_distr)
stdp = sim.STDPMechanism(weight_dependence=sim.AdditiveWeightDependence(w_min=0.0, w_max=0.1),
timing_dependence=sim.Vogels2011Rule(eta=0.0, rho=1e-3),
weight=0.005, delay=0.5)
import pyNN.neuron as sim # can of course replace `neuron` with `nest`, `brian`, etc.
import matplotlib.pyplot as plt
import numpy as np
sim.setup(timestep=0.01)
p_in = sim.Population(10, sim.SpikeSourcePoisson(rate=10.0), label="input")
p_out = sim.Population(10, sim.EIF_cond_exp_isfa_ista(), label="AdExp neurons")
syn = sim.StaticSynapse(weight=0.05)
random = sim.FixedProbabilityConnector(p_connect=0.5)
connections = sim.Projection(p_in, p_out, random, syn, receptor_type='excitatory')
p_in.record('spikes')
p_out.record('spikes') # record spikes from all neurons
p_out[0:2].record(['v', 'w', 'gsyn_exc']) # record other variables from first two neurons
sim.run(500.0)
spikes_in = p_in.get_data()
data_out = p_out.get_data()
fig_settings = {
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8
}
plt.rcParams.update(fig_settings)
plt.figure(1, figsize=(6, 8))
theta = 20.0 # firing thresholds
tau = 20.0 # membrane time constant
tau_syn = 0.5 # synapse time constant
nu_thresh = theta * v2i / (Je * Ce * tau * exp(1.0) * tau_syn) # threshold rate
nu_ext = eta * nu_thresh # external rate per synapse
input_rate = 1000.0 * nu_ext # mean input spiking rate
v_init = RandomDistribution("uniform", (theta/2, theta), rng=NumpyRNG(seed=79845634))
sim.setup()
celltype = sim.IF_curr_alpha(tau_m=tau, v_thresh=theta,
tau_refrac=2.0, v_reset=10.0,
v_rest=0.0, cm=tau/1.5,
tau_syn_E=tau_syn, tau_syn_I=tau_syn)
ext_stim = sim.SpikeSourcePoisson(rate=input_rate)
exc_cells = sim.Population(Ne, celltype, initial_values={'v': v_init}, label="Exc")
inh_cells = sim.Population(Ni, celltype, initial_values={'v': v_init}, label="Inh")
external = sim.Population(int(Cext), ext_stim, label="Ext")
all_cells = exc_cells + inh_cells
all_to_all = sim.AllToAllConnector(callback=ProgressBar())
random_uniform = sim.FixedProbabilityConnector(p_connect=epsilon, callback=ProgressBar())
static_ext = sim.StaticSynapse(delay=delay, weight=Jext)
static_exc = sim.StaticSynapse(delay=delay, weight=Je)
static_inh = sim.StaticSynapse(delay=delay, weight=Ji)
input_prj = sim.Projection(external, all_cells, all_to_all,