import scipy as sp
from scipy import signal
import seaborn as sns
import pickle
prefs.codegen.target = "numpy"
print('modules loaded')
#%% Simulation details
# import parameters_sim.py file
import parameters_sim
print('Parameters file imported')
parameters_sim.neuron_num(
) # neuron number specific to configuration and layer
simtime = 5000 * ms # sim. time
#%% Spike count per layer
#### requires all input frequencies and gvariables for a configuration ####
count_g = 0
gvariable = linspace(0, 10, 21)
input_r = linspace(0, 70, 8)
output_spikemon_L4 = zeros(
((len(input_r)), len(gvariable))) # define empty matrices to store data
output_spikemon_L23 = zeros(((len(input_r)), len(gvariable)))
for g in gvariable:
#%% define Coefficient of Variation
def CV(spikes):
'''
Coefficient of variation
'''
if spikes == []:
return 0
ISI = diff(spikes) # Interspike intervals
return std(ISI) / mean(ISI) # standard deviation ISI/mean ISI
#%% Analysis (over input frequencies 10-70Hz ; all synaptic ratios)
parameters_sim.neuron_num()
gvariable = linspace(0, 10, 21)
input_r = linspace(10, 70, 7)
# Initialize trains to 0
train_4_e = 0
train_4_i = 0
train_23_e = 0
train_23_i = 0
# Define empty matrices to store data
mean_cv_4_e = full(((len(input_r)), len(gvariable)), nan)
mean_cv_4_i = full(((len(input_r)), len(gvariable)), nan)
mean_cv_23_e = full(((len(input_r)), len(gvariable)), nan)
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import scipy as sp
from scipy import signal
import seaborn as sns
import pickle
prefs.codegen.target = "numpy" # avoid error message
print('Libraries loaded')
#%% Simulation details
# import parameters.py file
import parameters_sim
print('Parameters file imported')
parameters_sim.neuron_num() # input neuron numbers
simtime = 5000*ms # simulation time
dt_value = 0.1
#%% Coefficient of correlation Matrix
#### Calculated per configuration --- at a particular input frequency --- at a particular synaptic ratio ####
count_g = 0
gvariable = linspace(0,10,21)
for g in gvariable:
g = g
count_g = count_g + 1
print('Analysing data for g = ' + str(g))
import matplotlib import matplotlib.ticker import numpy as np import scipy as sp from scipy import signal import pickle from brian2 import * from brian2.core.network import TextReport prefs.codegen.target = "numpy" # avoid error message during code compilation #%% Parameters import parameters_sim parameters_sim.neuron_num() # neuron numbers from README.md parameters_sim.prob_vals() # network upscaling probablilty values # projection widths sigmaffwd_4, sigmaRe_4, sigmaRi_4 = .1 * mmeter, .05 * mmeter, .03 * mmeter # L4 sigmaffwd_23, sigmaRe_23, sigmaRi_23 = .05 * mmeter, .15 * mmeter, .03 * mmeter # L2/3 # LIF model characterisitics # excitatory neurons taumem_e = 15 * ms # membrane time constant vT_e = -50 * mV # threshold voltage El_e = -60 * mV # membrane potential vre_e = -65 * mV # reset voltage tref_e = 1.5 * ms # refractory period # inhibitory neurons
from random import sample
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from IPython.core.debugger import Pdb
ipdb = Pdb()
import scipy
import scipy as sp
from scipy import signal
#%% Sim. details
import parameters_sim # import parameter file
parameters_sim.neuron_num() # load neuron numbers
# Simtime
simtime = 5000 * ms
dt_value = 0.1
#%% Gaussian spikes function
def gaussian(spiketrain, truncate=3, sigma=500):
# spiketrain = binary spiketrain
# Width of function
window_size = 2 * int(truncate * sigma + 0.5) + 1
# Gaussian function