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run_simulation.py
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run_simulation.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from brian2 import *
from scipy.spatial import distance as dst
from scipy.spatial.transform import Rotation as R
from tqdm import tqdm
import os
import time
import sys
from shutil import copyfile
import argparse
import parameters
import re # regular expressions
from model.globals import *
from model.HH_equations import *
from model.kuramoto_equations import *
from model.filter_equations import *
from model.fixed_input_equations import *
from model.Vm_avg_eqs import *
from model import settings
from model import setup
from src.annex_funcs import make_flat
from src.myplot import *
from src import stimulation
# Reading Amelie's locations
def parse_positions(fname):
""" Opens file and parses coordinates """
pattern = r'[\[\],\n]' # to remove from read lines
x = []
y = []
z = []
with open(fname, 'r') as fp:
for line in fp:
line_tmp = re.sub('[\[\] \,\n]+', ' ', line)
tok = re.sub(pattern, '', line).split()
x.append(float(tok[0]))
y.append(float(tok[1]))
z.append(float(tok[2]))
return np.array([x, y, z]).T
# Configuration
# -------------------------------------------------------------#
# Use C++ standalone code generation TODO: Experimental!
#set_device('cpp_standalone')
# Parallel w/ Cython - independent caches
cache_dir = os.path.expanduser(f'~/.cython/brian-pid-{os.getpid()}')
prefs.codegen.runtime.cython.cache_dir = cache_dir
prefs.codegen.runtime.cython.multiprocess_safe = False
# Parse arguments
parser = argparse.ArgumentParser(description='MemStim using HH neurons')
parser.add_argument('-p', '--parameters',
nargs='?',
type=str,
default=os.path.join('configs', 'default.json'),
help='Parameters file (json format)')
parser.add_argument('-sd', '--save_dir',
nargs='?',
type=str,
default='results',
help='Destination directory to save the results')
args = parser.parse_args()
filename = args.parameters
resdir = args.save_dir
try:
data = parameters.load(filename)
print('Using "{0}"'.format(filename))
except Exception as e:
print(bcolors.RED + '[!]' + "Error code " + str(e.errno) + ": " + e.strerror + ' | Using "default.json"' + bcolors.ENDC)
data = parameters._data
filename = os.path.join('configs', 'default.json')
parameters.dump(data)
print()
# locals().update(data)
# Settings initialization
settings.init(data)
# Create the necessary directories
print('\n[00] Making directories...')
print('-'*32)
dirs = {}
# dirs['results'] = 'results_opt_DG'
dirs['results'] = resdir
if not os.path.isdir(dirs['results']):
print('[+] Creating directory', dirs['results'])
os.makedirs(dirs['results'])
if settings.I_stim[0]:
dirs['stim'] = os.path.join(dirs['results'], '{stimamp:.1f}_nA'.format(stimamp=settings.I_stim[0]))
if not os.path.isdir(dirs['stim']):
print('[+] Creating directory', dirs['stim'])
os.makedirs(dirs['stim'])
dirs['offset'] = os.path.join(dirs['stim'], '{phase:.2f}_{stim_on:.1f}_ms'.format(phase=settings.offset, stim_on=settings.stim_onset*1e3))
if not os.path.isdir(dirs['offset']):
print('[+] Creating directory', dirs['offset'])
os.makedirs(dirs['offset'])
dirs['base'] = dirs['offset']
else:
dirs['stim'] = os.path.join(dirs['results'], 'None')
if not os.path.exists(dirs['stim']):
print('[+] Creating directory', dirs['stim'])
os.makedirs(dirs['stim'])
dirs['base'] = dirs['stim']
dtime = datetime.datetime.now().strftime("%d-%m-%Y %HH%MM%SS") # imported from brian2
dirs['base'] = os.path.join(dirs['base'], dtime)
if not os.path.exists(dirs['base']):
print('[+] Creating directory', dirs['base'])
os.makedirs(dirs['base'])
dirs['figures'] = os.path.join(dirs['base'], 'figures')
if not os.path.isdir(dirs['figures']):
print('[+] Creating directory', dirs['figures'])
os.makedirs(dirs['figures'])
dirs['data'] = os.path.join(dirs['base'], 'data')
if not os.path.isdir(dirs['data']):
print('[+] Creating directory', dirs['data'])
os.makedirs(dirs['data'])
dirs['positions'] = os.path.join(dirs['data'], 'positions')
if not os.path.isdir(dirs['positions']):
print('[+] Creating directory', dirs['positions'])
os.makedirs(dirs['positions'])
dirs['spikes'] = os.path.join(dirs['data'], 'spikes')
if not os.path.isdir(dirs['spikes']):
print('[+] Creating directory', dirs['spikes'])
os.makedirs(dirs['spikes'])
dirs['currents'] = os.path.join(dirs['data'], 'currents')
if not os.path.isdir(dirs['currents']):
print('[+] Creating directory', dirs['currents'])
os.makedirs(dirs['currents'])
# Copy the configuration file on the results directory for safekeeping
copyfile(filename, os.path.join(dirs['base'], 'parameters_bak.json'))
# Debugging?
# -------------------------------------------------------------#
#if settings.debugging:
if settings.debugging:
prefs.codegen.target = 'numpy' # Use Python code generation instead of Cython
prefs.codegen.loop_invariant_optimisations = False # Switch off some optimization that makes the link between code and equations less obvious
np.seterr(all='raise', under='ignore') # Make numpy raise errors for all kind of floating point problems, including division by 0, but ignoring underflows
print('###########################')
print(' [!] DEBUGGING MODE ON')
print('###########################')
# Make the neuron groups
# -------------------------------------------------------------#
print('\n[10] Making the neuron groups...')
print('-'*32)
G_all = [[[] for pops in range(2)] for areas in range(4)]
#fig, axs = subplots(nrows=1, ncols=1)
fig_anat = figure()
ax_anat = fig_anat.add_subplot(111, projection='3d')
# rotation matrix for fixing rotated y-positions from stippling program
r = R.from_euler('x', 180, degrees=True)
def parse_coords(fname, NG):
""" Opens text file and parses coordinates """
pattern = r'[\[\],\n]' # to remove from read lines
with open(fname, 'r') as fin:
idx = 0
for line in fin:
tok = re.sub(pattern, '', line).split()
NG.x_soma[idx] = float(tok[0])*scale
NG.y_soma[idx] = float(tok[1])*scale
NG.z_soma[idx] = float(tok[2])*scale
idx += 1
print('[+] Groups:')
# EC -> receives theta input from MS
# E
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'EC_E-stipple-10000.npy'))
pos = hstack((pos, zeros((settings.N_EC[0], 1))))
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_EC[0])
# pos = parse_positions(os.path.join('positions', 'EC_exc.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_E = NeuronGroup(N=settings.N_EC[0],
model=py_CAN_inp_eqs,
threshold='v>V_th',
reset=reset_eqs,
refractory=refractory_time,
method=integ_method,
name='EC_pyCAN')
G_E.size = cell_size_py
G_E.glu = 1
# G_E.sigma = settings.sigma_EC[0]*uvolt
G_E.x_soma = pos[:,0]*metre
G_E.y_soma = pos[:,1]*metre
G_E.z_soma = pos[:,2]*metre
# G_E.x_soma = pos[:,0]*scale_aussel
# G_E.y_soma = pos[:,1]*scale_aussel
# G_E.z_soma = pos[:,2]*scale_aussel
# I
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'EC_I-stipple-1000.npy'))
pos = hstack((pos, zeros((settings.N_EC[1], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_EC[1])
# pos = parse_positions(os.path.join('positions', 'EC_inh.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_I = NeuronGroup(N=settings.N_EC[1],
model=inh_inp_eqs,
threshold='v>V_th',
refractory=refractory_time,
method=integ_method,
name='EC_inh')
G_I.size = cell_size_inh
# G_I.sigma = settings.sigma_EC[1]*uvolt
G_I.x_soma = pos[:,0]*metre
G_I.y_soma = pos[:,1]*metre
G_I.z_soma = pos[:,2]*metre
# G_I.x_soma = pos[:,0]*scale_aussel
# G_I.y_soma = pos[:,1]*scale_aussel
# G_I.z_soma = pos[:,2]*scale_aussel
# Plot X,Y,Z
ax_anat.scatter(G_E.x_soma, G_E.y_soma, G_E.z_soma, c='blue')
ax_anat.scatter(G_I.x_soma, G_I.y_soma, G_I.z_soma, c='red')
# Add to list
G_all[0][0].append(G_E)
G_all[0][1].append(G_I)
print('[\u2022]\tEC: done')
# DG
# E
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'DG_E-stipple-10000.npy'))
pos = hstack((pos, zeros((settings.N_DG[0], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_DG[0])
# pos = parse_positions(os.path.join('positions', 'DG_exc.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_E = NeuronGroup(N=settings.N_DG[0],
model=py_eqs,
threshold='v>V_th',
reset=reset_eqs,
refractory=refractory_time,
method=integ_method,
name='DG_py')
G_E.size = cell_size_py
G_E.glu = 1
# G_E.sigma = settings.sigma_DG[0]*uvolt
G_E.x_soma = pos[:,0]*metre
G_E.y_soma = pos[:,1]*metre
G_E.z_soma = pos[:,2]*metre
# G_E.x_soma = pos[:,0]*scale_aussel
# G_E.y_soma = pos[:,1]*scale_aussel
# G_E.z_soma = pos[:,2]*scale_aussel
# I
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'DG_I-stipple-100.npy'))
pos = hstack((pos, zeros((settings.N_DG[1], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_DG[1])
# pos = parse_positions(os.path.join('positions', 'DG_inh.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_I = NeuronGroup(N=settings.N_DG[1],
model=inh_eqs,
threshold='v>V_th',
refractory=refractory_time,
method=integ_method,
name='DG_inh')
G_I.size = cell_size_inh
# G_I.sigma = settings.sigma_DG[1]*uvolt
G_I.x_soma = pos[:,0]*metre
G_I.y_soma = pos[:,1]*metre
G_I.z_soma = pos[:,2]*metre
# G_I.x_soma = pos[:,0]*scale_aussel
# G_I.y_soma = pos[:,1]*scale_aussel
# G_I.z_soma = pos[:,2]*scale_aussel
# Plot X,Y,Z
ax_anat.scatter(G_E.x_soma, G_E.y_soma, G_E.z_soma, c='green')
ax_anat.scatter(G_I.x_soma, G_I.y_soma, G_I.z_soma, c='red')
# Add to list
G_all[1][0].append(G_E)
G_all[1][1].append(G_I)
print('[\u2022]\tDG: done')
# CA3
# E
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'CA3_E-stipple-1000.npy'))
pos = hstack((pos, zeros((settings.N_CA3[0], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_CA3[0])
# pos = parse_positions(os.path.join('positions', 'CA3_exc.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_E = NeuronGroup(N=settings.N_CA3[0],
model=py_CAN_eqs,
threshold='v>V_th',
reset=reset_eqs,
refractory=refractory_time,
method=integ_method,
name='CA3_pyCAN')
G_E.size = cell_size_py
G_E.glu = 1
# G_E.sigma = settings.sigma_CA3[0]*uvolt
G_E.x_soma = pos[:,0]*metre
G_E.y_soma = pos[:,1]*metre
G_E.z_soma = pos[:,2]*metre
# G_E.x_soma = pos[:,0]*scale_aussel
# G_E.y_soma = pos[:,1]*scale_aussel
# G_E.z_soma = pos[:,2]*scale_aussel
# I
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'CA3_I-stipple-100.npy'))
pos = hstack((pos, zeros((settings.N_CA3[1], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_CA3[1])
# pos = parse_positions(os.path.join('positions', 'CA3_inh.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_I = NeuronGroup(N=settings.N_CA3[1],
model=inh_eqs,
threshold='v>V_th',
refractory=refractory_time,
method=integ_method,
name='CA3_inh')
G_I.size = cell_size_inh
# G_I.sigma = settings.sigma_CA3[1]*uvolt
G_I.x_soma = pos[:,0]*metre
G_I.y_soma = pos[:,1]*metre
G_I.z_soma = pos[:,2]*metre
# G_I.x_soma = pos[:,0]*scale_aussel
# G_I.y_soma = pos[:,1]*scale_aussel
# G_I.z_soma = pos[:,2]*scale_aussel
# Plot X,Y,Z
ax_anat.scatter(G_E.x_soma, G_E.y_soma, G_E.z_soma, c='blue')
ax_anat.scatter(G_I.x_soma, G_I.y_soma, G_I.z_soma, c='red')
# Add to list
G_all[2][0].append(G_E)
G_all[2][1].append(G_I)
print('[\u2022]\tCA3: done')
# CA1
# E
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'CA1_E-stipple-10000.npy'))
pos = hstack((pos, zeros((settings.N_CA1[0], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_CA1[0])
# pos = parse_positions(os.path.join('positions', 'CA1_exc.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_E = NeuronGroup(N=settings.N_CA1[0],
model=py_CAN_eqs,
threshold='v>V_th',
reset=reset_eqs,
refractory=refractory_time,
method=integ_method,
name='CA1_pyCAN')
G_E.size = cell_size_py
G_E.glu = 1
# G_E.sigma = settings.sigma_CA1[0]*uvolt
G_E.x_soma = pos[:,0]*metre
G_E.y_soma = pos[:,1]*metre
G_E.z_soma = pos[:,2]*metre
# G_E.x_soma = pos[:,0]*scale_aussel
# G_E.y_soma = pos[:,1]*scale_aussel
# G_E.z_soma = pos[:,2]*scale_aussel
# I
pos = np.load(os.path.join('model', 'neuron_positions', 'full', 'CA1_I-stipple-1000.npy'))
pos = hstack((pos, zeros((settings.N_CA1[1], 1)))) # add z-axis
pos = r.apply(pos)
pos *= scale
pos[:,2] += 15*mm*rand(settings.N_CA1[1])
# pos = parse_positions(os.path.join('positions', 'CA1_inh.txt'))
idx = np.argsort(pos[:,2]) # sort neurons by increasing z-coordinate
pos = pos[idx]
G_I = NeuronGroup(N=settings.N_CA1[1],
model=inh_eqs,
threshold='v>V_th',
refractory=refractory_time,
method=integ_method,
name='CA1_inh')
G_I.size = cell_size_inh
# G_I.sigma = settings.sigma_CA1[1]*uvolt
G_I.x_soma = pos[:,0]*metre
G_I.y_soma = pos[:,1]*metre
G_I.z_soma = pos[:,2]*metre
# G_I.x_soma = pos[:,0]*scale_aussel
# G_I.y_soma = pos[:,1]*scale_aussel
# G_I.z_soma = pos[:,2]*scale_aussel
# Plot X,Y,Z
ax_anat.scatter(G_E.x_soma, G_E.y_soma, G_E.z_soma, c='blue')
ax_anat.scatter(G_I.x_soma, G_I.y_soma, G_I.z_soma, c='red')
# Add to list
G_all[3][0].append(G_E)
G_all[3][1].append(G_I)
print('[\u2022]\tCA1: done')
# Flatten
G_flat = make_flat(G_all)
# initialize the groups, set initial conditions
for ngroup in G_flat:
ngroup.v = '-60.*mvolt-rand()*10*mvolt' # str -> individual init. val. per neuron, randn is Gaussian
# CA1 populations get stimulated
if (ngroup.name=='{group}_pyCAN'.format(group=settings.stim_target) or ngroup.name=='{group}_py'.format(group=settings.stim_target)) or ngroup.name=='{group}_inh'.format(group=settings.stim_target):
print("[!] Stimulation applied @", ngroup.name)
ngroup.r = 1 # 1 means on
# calculate the distance
# d0 = '1/({sigma}*(siemens/metre)*4*pi*sqrt((x_soma-{x0}*mm)**2 + (y_soma-{y0}*mm)**2 + (z_soma-{z0}*mm)**2))'.format(rho=settings.stim_sigma, x0=settings.stim_coordinates[0], y0=settings.stim_coordinates[1], z0=settings.stim_coordinates[2])
# alternatively, calculate distances like so:
# neuron_pos = column_stack((ngroup.x_soma/mm, ngroup.y_soma/mm, ngroup.z_soma/mm))
# elec_pos = array(settings.stim_coordinates)[np.newaxis,...]
# d0 = dst.cdist(elec_pos, neuron_pos)
# ngroup.r = clip(100/(4*pi*d0), a_min=0, amax=1) # clip the values, r is NOT a gain
else:
ngroup.r = 0 # int -> same init. val. for all neurons
# neuron_pos = column_stack((ngroup.x_soma/mm, ngroup.y_soma/mm, ngroup.z_soma/mm))
# elec_pos = array(settings.stim_coordinates)[np.newaxis,...]
# d0 = dst.cdist(elec_pos, neuron_pos)
# ngroup.r = clip(100/(4*pi*d0), a_min=0, a_max=1) # clip the values, r is NOT a gain
# DEBUGGING DISTANCES
print('\n[11] Intra-region distances...')
for group in G_flat:
# organize positions for this specific group
neuron_pos = column_stack((group.x_soma, group.y_soma, group.z_soma))
# calculate pair-wise distances using pdist
dist_res = dst.pdist(neuron_pos, 'euclidean')
# if using cdist: generate a mask to skip the diagonal for minimum calculation
#mask = ones(dist_res.shape, dtype=bool)
#fill_diagonal(mask, False)
# calculate intra-area distance boundaries
min_dist, max_dist = (dist_res.min(), dist_res.max())
print('{:10} pdist: ({:20} , {:20})\n\tx ---> ({:28}, {:28})\n\ty ---> ({:28}, {:28})\n\tz ---> ({:28}, {:28})\n'.format(
'{}'.format(group.name),
'{}'.format(min_dist),
'{}'.format(max_dist),
'{}'.format(group.x_soma[:].min()),
'{}'.format(group.x_soma[:].max()),
'{}'.format(group.y_soma[:].min()),
'{}'.format(group.y_soma[:].max()),
'{}'.format(group.z_soma[:].min()),
'{}'.format(group.z_soma[:].max())))
# Make the synapses
# -------------------------------------------------------------#
print('\n[12] Making the synapses...')
# gains
gains_all = [[1./G, 1.], [G, G], [1./G, 1.], [1., G]]
print("[!] Gains:", gains_all)
# intra
print('[+] Intra-region')
syn_EC_all = setup.connect_intra(G_all[0][0], G_all[0][1], settings.p_EC_all, gains_all[0])
print('[\u2022]\tEC-to-EC: done')
syn_DG_all = setup.connect_intra(G_all[1][0], G_all[1][1], settings.p_DG_all, gains_all[1])
print('[\u2022]\tDG-to-DG: done')
syn_CA3_all = setup.connect_intra(G_all[2][0], G_all[2][1], settings.p_CA3_all, gains_all[2])
print('[\u2022]\tCA3-to-CA3: done')
syn_CA1_all = setup.connect_intra(G_all[3][0], G_all[3][1], settings.p_CA1_all, gains_all[3])
print('[\u2022]\tCA1-to-CA1: done')
syn_intra_all = [syn_EC_all, syn_DG_all, syn_CA3_all, syn_CA1_all]
# inter
print('[+] Inter-region')
syn_EC_DG_all = setup.connect_inter(G_all[0][0], G_all[1][0], G_all[1][1], settings.p_inter_all[0][1], gains_all[0])
syn_EC_CA3_all = setup.connect_inter(G_all[0][0], G_all[2][0], G_all[2][1], settings.p_inter_all[0][2], gains_all[0])
syn_EC_CA1_all = setup.connect_inter(G_all[0][0], G_all[3][0], G_all[3][1], settings.p_inter_all[0][3], gains_all[0])
print('[\u2022]\tEC-to-all: done')
syn_DG_CA3_all = setup.connect_inter(G_all[1][0], G_all[2][0], G_all[2][1], settings.p_inter_all[1][2], gains_all[1])
print('[\u2022]\tDG-to-CA3: done')
syn_CA3_CA1_all = setup.connect_inter(G_all[2][0], G_all[3][0], G_all[3][1], settings.p_inter_all[2][3], gains_all[2])
print('[\u2022]\tCA3-to-CA1: done')
syn_CA1_EC_all = setup.connect_inter(G_all[3][0], G_all[0][0], G_all[0][1], settings.p_inter_all[3][0], gains_all[3])
print('[\u2022]\tCA1-to-EC: done')
syn_inter_all = [syn_EC_DG_all, syn_EC_CA3_all, syn_EC_CA1_all, syn_DG_CA3_all, syn_CA3_CA1_all, syn_CA1_EC_all]
# Add the monitors (spikes/rates)
# -------------------------------------------------------------#
print('\n[13] Adding monitors...')
state_mon_all = []
spike_mon_all = []
rate_mon_all = []
state_mon_E_all = [[StateMonitor(G_py, ['v'], record=True) for G_py in G_all[i][0] if G_py] for i in range(4)]
state_mon_I_all = [[StateMonitor(G_inh, ['v'], record=True) for G_inh in G_all[i][1] if G_inh] for i in range(4)]
print('[\u2022]\tState monitors [v]: done')
spike_mon_E_all = [[SpikeMonitor(G_py, name=G_py.name+'_spikemon') for G_py in G_all[i][0] if G_py] for i in range(4)]
spike_mon_I_all = [[SpikeMonitor(G_inh, name=G_inh.name+'_spikemon') for G_inh in G_all[i][1] if G_inh] for i in range(4)]
print('[\u2022]\tSpike monitors: done')
rate_mon_E_all = [[PopulationRateMonitor(G_py) for G_py in G_all[i][0] if G_py] for i in range(4)]
rate_mon_I_all = [[PopulationRateMonitor(G_inh) for G_inh in G_all[i][1] if G_inh] for i in range(4)]
print('[\u2022]\tRate monitors: done')
# Add groups for monitoring the avg Vm
# -------------------------------------------------------------#
# Average Vm per group per area
print('\n[20] Vm average groups...')
print('-'*32)
G_Vm_avg = [[[] for pops in range(2)] for areas in range(4)]
syn_Vm_avg_all = [[[] for pops in range(2)] for areas in range(4)]
state_mon_Vm_avg = [[[] for pops in range(2)] for areas in range(4)]
for area_idx in range(4):
print('[+] {0}:'.format(areas[area_idx]))
G_E = NeuronGroup(1, eq_record_neurons, name='Vm_avg_{0}_E'.format(areas[area_idx]))
G_I = NeuronGroup(1, eq_record_neurons, name='Vm_avg_{0}_I'.format(areas[area_idx]))
G_Vm_avg[area_idx][0].append(G_E)
G_Vm_avg[area_idx][1].append(G_I)
print('[\u2022]\tNeuronGroups: done')
Syn_E = Synapses(G_all[area_idx][0][0], G_E, model=eq_record_synapses)
Syn_E.connect()
Syn_I = Synapses(G_all[area_idx][1][0], G_I, model=eq_record_synapses)
Syn_I.connect()
syn_Vm_avg_all[area_idx][0].append(Syn_E)
syn_Vm_avg_all[area_idx][1].append(Syn_I)
print('[\u2022]\tSynapses: done')
SM_Vm_avg_E = StateMonitor(G_E, ['sum_v'], record=True, name='Vm_avg_mon_{0}_E'.format(areas[area_idx]))
SM_Vm_avg_I = StateMonitor(G_I, ['sum_v'], record=True, name='Vm_avg_mon_{0}_I'.format(areas[area_idx]))
state_mon_Vm_avg[area_idx][0].append(SM_Vm_avg_E)
state_mon_Vm_avg[area_idx][1].append(SM_Vm_avg_I)
print('[\u2022]\tMonitors: done')
# Make the spikes-to-rates group
# -------------------------------------------------------------#
print('\n[30] Spikes-to-rates group...')
print('-'*32)
G_S2R = NeuronGroup(1,
model=firing_rate_filter_eqs,
method='exact',
#method='integ_method',
name='S2R_filter',
namespace=filter_params)
G_S2R.Y = 0 # initial conditions
print('[\u2022]\tGroup: done')
print('\n[31] Making the synapses...')
# find the CA1-E group
G_CA1_E = None
for g in G_flat:
if g.name=='CA1_pyCAN':
G_CA1_E = g
break
# connect the CA1-E group to the low-pass-filter spikes-2-rates (S2R) group
if G_CA1_E:
syn_CA1_2_rates = Synapses(G_CA1_E, G_S2R, on_pre='Y_post += (1/tauFR)/N_incoming', namespace=filter_params)
syn_CA1_2_rates.connect()
print('[\u2022]\tConnecting CA1-to-S2R: done')
# spikes2rates monitor (vout)
print('\n[32] Adding monitors...')
state_mon_s2r = StateMonitor(G_S2R, ['drive'], record=True)
print('[\u2022]\tState monitor [drive]: done')
# Inputs
# -------------------------------------------------------------#
print('\n[40] Inputs...')
print('-'*32)
G_inputs = []
syn_inputs = []
state_mon_inputs = []
print('[+] Time-vector')
tv = linspace(0, settings.duration/second, int(settings.duration/(settings.dt))+1)
if settings.fixed_input_enabled:
# Fixed input
print('\n[41] Making the fixed input group...')
A0 = settings.fixed_input_low
A1 = settings.fixed_input_high
f_rhythm = settings.fixed_input_frequency
print(bcolors.YELLOW + '[!]' + bcolors.ENDC + ' Using fixed input at %dHz | range [%.2f, %.2f]' % (f_rhythm, A0, A1))
# Make the input TimedArray
inp_theta_rect = (A1-A0)*(sin(2*pi*f_rhythm/Hz*tv-pi/2)+1)/2+A0
trail_zeros = zeros(int(settings.fixed_input_delay/(settings.dt*second)))
inp_theta_delayed = concatenate((trail_zeros, inp_theta_rect/nA))
inp_theta = TimedArray(inp_theta_delayed*nA, dt=settings.dt) # external theta (TESTING)
# THERE WAS A 0.5 GAIN HERE!!!
# Make the input group and append it to the list
G_input = NeuronGroup(1, model=fixed_input_TA_eqs,
threshold='False',
method='euler',
name='Fixed_input_%dHz' % f_rhythm)
G_inputs.append(G_input)
print('[\u2022]\tFixed input group: done')
# state monitor
state_mon_theta_rhytm = StateMonitor(G_input, ['rhythm'], record=True)
state_mon_inputs.append(state_mon_theta_rhytm)
print('[\u2022]\tState monitor [rhythm]: done')
else:
# Kuramoto Oscillators
print('\n[41] Making the Kuramoto oscillators group...')
print(bcolors.YELLOW + '[!]' + bcolors.ENDC + ' Using dynamic input; Kuramoto oscillators of size N=%d w/ f0 = %.2f Hz | rhythm gain: %.2f nA | reset gain: %.2f' % (settings.N_Kur, settings.f0, settings.r_gain/nA, settings.k_gain))
# Make the necessary groups
# f0 = settings.f0 # settings.f0 does not work inside equations
# sigma = settings.sigma
G_K = NeuronGroup(settings.N_Kur,
model=kuramoto_eqs_stim,
threshold='True',
method='euler',
name='Kuramoto_oscillators_N_%d' % settings.N_Kur)
#G_K.Theta = '2*pi*rand()' # uniform U~[0,2π]
#G_K.omega = '2*pi*(f0+sigma*randn())' # normal N~(f0,σ)
theta0 = 2*pi*rand(settings.N_Kur) # uniform U~[0,2π]
omega0 = 2*pi*(settings.f0 + settings.sigma*randn(settings.N_Kur)) # ~N(2πf0,σ)
G_K.Theta = theta0
G_K.omega = omega0
G_K.kN = settings.kN_frac
G_K.G_in = settings.k_gain
G_K.offset = settings.offset
G_inputs.append(G_K) # append to the group list!
print('[\u2022]\tKuramoto oscillators group: done')
syn_kuramoto = Synapses(G_K, G_K, on_pre=syn_kuramoto_eqs, method='euler', name='Kuramoto_intra')
syn_kuramoto.connect(condition='i!=j')
syn_inputs.append(syn_kuramoto)
print('[\u2022]\tSynapses (Kuramoto): done')
# Kuramoto order parameter group
G_pop_avg = NeuronGroup(1,
model=pop_avg_eqs,
#method='euler',
name='Kuramoto_averaging')
r0 = 1/settings.N_Kur * sum(exp(1j*G_K.Theta))
G_pop_avg.x = real(r0) # avoid division by zero
G_pop_avg.y = imag(r0)
G_pop_avg.G_out = settings.r_gain
G_input = G_pop_avg # G_input as alias for G_pop_avg
G_inputs.append(G_input) # append to the group list!
print('[\u2022]\tOrder parameter group: done')
syn_avg = Synapses(G_K, G_pop_avg, syn_avg_eqs, name='Kuramoto_avg')
syn_avg.connect()
syn_inputs.append(syn_avg)
print('[\u2022]\tSynapses (OP): done')
# Connections
print('\n[42] Connecting oscillators and filters...')
print('-'*32)
# connect the S2R group to the Kuramoto oscillators by linking input X to firing rates (drive)
G_K.X = linked_var(G_S2R, 'drive')
print('[\u2022]\tLinking S2R to Kuramoto oscillators: done')
# Monitors
# Kuramoto monitors
print('\n[43] Kuramoto and Filter Monitors...')
print('-'*32)
state_mon_kuramoto = StateMonitor(G_K, ['Theta'], record=True)
state_mon_order_param = StateMonitor(G_pop_avg, ['coherence', 'phase', 'rhythm', 'rhythm_rect'], record=True)
state_mon_inputs.append(state_mon_kuramoto)
state_mon_inputs.append(state_mon_order_param)
print('[\u2022]\tState monitor [Theta]: done')
# connect the fixed input to the I_exc variable in EC_E and EC_I
for g in G_flat:
if g.name=='EC_pyCAN' or g.name=='EC_inh':
print('[+]\tLinking input (theta rhythm) to group ', g.name)
g.I_exc = linked_var(G_input, 'rhythm')
print('[\u2022]\tLinking input rhythm: done')
print('[\u2022]\tInputs: done')
# Stimulation and other inputs
# -------------------------------------------------------------#
print('\n[50] Stimulation...')
print('-'*32)
# generate stimulation signal
if settings.I_stim[0]:
print(bcolors.GREEN + '[+]' + bcolors.ENDC + ' Stimulation ON')
xstim, tv_stim = stimulation.generate_stim(duration=settings.stim_duration,
dt=settings.stim_dt,
I_stim=settings.I_stim,
stim_on=settings.stim_onset,
nr_of_trains=settings.nr_of_trains,
nr_of_pulses=settings.nr_of_pulses,
stim_freq=settings.stim_freq,
pulse_width=settings.pulse_width,
pulse_freq=settings.pulse_freq,
ipi=settings.stim_ipi)
else:
print(bcolors.RED + '[-]' + bcolors.ENDC + ' No stimulation defined; using empty TimedArray')
xstim = zeros(tv.shape)
tv_stim = tv
inputs_stim = TimedArray(values=xstim*nA, dt=settings.stim_dt*second, name='Input_stim')
# Add any extra monitors here
# -------------------------------------------------------------#
print('\n[60] Adding extra monitors...')
# state_mon_EC_E_curr = StateMonitor(G_all[0][0][0], ['I_CAN', 'I_M', 'I_leak', 'I_K', 'I_Na', 'I_Ca', 'I_SynE', 'I_SynI', 'I_SynExt', 'I_SynHipp', 'I_exc', 'I_stim', 'Ca_i'], record=np.arange(4995,5006), name='EC_E_currents')
# print('[\u2022]\tState monitor [EC-E currents]: done')
# state_mon_CA1_E_curr = StateMonitor(G_all[3][0][0], ['I_CAN', 'I_M', 'I_leak', 'I_K', 'I_Na', 'I_Ca', 'I_SynE', 'I_SynI', 'I_SynExt', 'I_SynHipp', 'I_stim', 'Ca_i'], record=np.concatenate((np.arange(4995,5006), np.array([8310]))), name='CA1_E_currents')
# print('[\u2022]\tState monitor [CA1-E currents]: done')
# state_mon_noise_all = [StateMonitor(G, ['noise'], record=True) for G in G_flat]
# print('[\u2022]\tNoise monitors: done')
# state_mon_Vm_EC_exc = StateMonitor(G_all[0][0][0], ['v'], record=np.concatenate((np.arange(0,10), np.arange(5000, 5010), np.arange(9000, 9010))))
# state_mon_Vm_EC_inh = StateMonitor(G_all[0][1][0], ['v'], record=np.concatenate((np.arange(0,10), np.arange(500, 510), np.arange(900, 910))))
# state_mon_Vm_CA1_exc = StateMonitor(G_all[3][0][0], ['v'], record=np.concatenate((np.arange(0,10), np.arange(5000, 5010), np.arange(9000,9010))))
# state_mon_Vm_CA1_inh = StateMonitor(G_all[3][1][0], ['v'], record=np.concatenate((np.arange(0,10), np.arange(500, 510), np.arange(900, 910))))
# print('[\u2022]\tVm monitors: done')
# state_mon_EC_ICAN = StateMonitor(G_all[0][0][0], ['I_CAN'], record=[0])
# print('[\u2022]\tEC I_CAN monitor: done')
# Create the Network
# -------------------------------------------------------------#
print('\n[70] Connecting the network...')
print('-'*32)
net = Network()
net.add(G_all) # add groups
net.add(G_S2R) # was missing
net.add(G_inputs)
net.add(G_Vm_avg)
print('[\u2022]\tNetwork groups: done')
for syn_intra_curr in make_flat(syn_intra_all): # add synapses (intra)
if syn_intra_curr != 0:
net.add(syn_intra_curr)
for syn_inter_curr in make_flat(syn_inter_all): # add synapses (inter)
if syn_inter_curr != 0:
net.add(syn_inter_curr)
for syn_Vm_avg in make_flat(syn_Vm_avg_all): # add synapses Vm avg
if syn_Vm_avg !=0:
net.add(syn_Vm_avg)
net.add(syn_CA1_2_rates)
net.add(syn_inputs) # synapses avout inputs
print('[\u2022]\tNetwork connections: done')
net.add(state_mon_E_all) # monitors
net.add(state_mon_I_all)
net.add(spike_mon_E_all)
net.add(spike_mon_I_all)
net.add(rate_mon_E_all)
net.add(rate_mon_I_all)
net.add(state_mon_s2r)
net.add(state_mon_inputs)
# net.add(state_mon_CA1_E_curr)
net.add(state_mon_Vm_avg)
# net.add(state_mon_EC_E_curr, state_mon_CA1_E_curr)
print('[\u2022]\tNetwork monitors: done')
# Run the simulation
# -------------------------------------------------------------#
defaultclock.dt = settings.dt
tstep = defaultclock.dt
print('\n[80] Starting simulation...')
print('-'*32)
start = time.time()
t_elapsed = 0*ms
net.run(settings.duration, report='text', report_period=10*second, profile=True)
# # Theta OFF
# print("[+] Theta off; nothing happening")
# G_K.kN = 0
# G_pop_avg.G_out = 0.
# t_run = 200*ms
# net.run(t_run, report='text', report_period=10*second, profile=True)
# t_elapsed += t_run
#
# # Theta ON + stim
# print("[+] Theta on + stim.")
# G_K.kN = settings.kN_frac
# G_pop_avg.G_out = settings.r_gain
# t_run = settings.duration - (500*ms + t_elapsed)
# net.run(t_run, report='text', report_period=10*second, profile=True)
# t_elapsed += t_runinp_theta
#
# # Theta OFF
# print("[+] Theta off | relaxation")
# G_pop_avg.G_out = 0.
# t_run = settings.duration - t_elapsed
# net.run(t_run, report='text', report_period=10*second, profile=True)
# t_elapsed += t_run
end = time.time()
print('-'*32)
print(bcolors.GREEN + '[+]' + ' Simulation ended' + bcolors.ENDC)
print(bcolors.YELLOW + '[!]' + bcolors.ENDC + ' Simulation ran for '+str((end-start)/60)+' minutes')
print()
print(profiling_summary(net=net, show=4)) # show the top 10 objects that took the longest
print('\n[81] Mean firing rates...')
print('-'*32)
for area in range(len(G_all)):
# Calculate mean firing rates
FR_exc_mean = (len(spike_mon_E_all[area][0].t)/settings.duration)/spike_mon_E_all[area][0].source.N
FR_inh_mean = (len(spike_mon_I_all[area][0].t)/settings.duration)/spike_mon_I_all[area][0].source.N
print(spike_mon_E_all[area][0].name.split('_')[0], 'E: ', FR_exc_mean, '\t', 'I: ', FR_inh_mean)
print('='*16)
# Plot the results
# -------------------------------------------------------------#
print('\n[90] Post-simulation actions')
print('-'*32)
print('\n[91] Plotting results...')
tight_layout()
# Plot the 3D shape
print("[+] Saving figure 'figures/anatomy.png'")
fig_anat.savefig(os.path.join(dirs['figures'], 'anatomy.png'))
# raster plot of all regions
# raster_fig, raster_axs, fig_name = plot_raster_all(spike_mon_E_all, spike_mon_I_all)
raster_fig, raster_axs, fig_name = plot_raster_all(spike_mon_E_all, spike_mon_I_all)
print("[+] Saving figure 'figures/%s'" %fig_name)
plot_watermark(raster_fig, os.path.basename(__file__), filename, settings.git_branch, settings.git_short_hash)
raster_fig.savefig(os.path.join(dirs['figures'], fig_name))
'''
# calculate order parameter in the end
samples = len(state_mon_kuramoto.Theta[0])
r = np.zeros(samples, dtype='complex')
for s in range(samples):
r[s] = 1/N_Kur * sum(exp(1j*state_mon_kuramoto.Theta[:,s])) # order parameter r(t)
'''
# Fig2 version
if settings.fixed_input_enabled:
# Plot the non-Kuramoto theta drive
fig_theta, ax_theta = subplots(1,1, figsize=(12,9))
ax_theta.plot(tv, inp_theta_delayed[:len(tv)])
print("[+] Saving figure 'figures/theta_inp.png'")
fig_theta.savefig(os.path.join(dirs['figures'], 'theta_inp.png'))
fig2, axs2, fig_name = plot_fig2(spike_mon_E_all, spike_mon_I_all, state_mon_s2r, state_mon_theta_rhytm, tv_stim, xstim)
plot_watermark(fig2, os.path.basename(__file__), filename, settings.git_branch, settings.git_short_hash)
else:
# kuramoto order parameter plots
kuramoto_fig, kuramoto_axs, fig_name = plot_kuramoto(state_mon_order_param)
plot_watermark(kuramoto_fig, os.path.basename(__file__), filename, settings.git_branch, settings.git_short_hash)
print("[+] Saving figure 'figures/%s'" %fig_name)
kuramoto_fig.savefig(os.path.join(dirs['figures'], fig_name))
fig2, axs2, fig_name = plot_fig2(spike_mon_E_all, spike_mon_I_all, state_mon_s2r, state_mon_order_param, tv_stim, xstim, mode="phase")
plot_watermark(fig2, os.path.basename(__file__), filename, settings.git_branch, settings.git_short_hash)
print("[+] Saving figure 'figures/%s'" %fig_name)
fig2.savefig(os.path.join(dirs['figures'], fig_name))
# # Plot more stuff
# fig_extra, extra_axs, fig_name = plot_network_output(spike_mon_E_all[-1][0], spike_mon_I_all[-1][0], state_mon_s2r, state_mon_order_param, tv_stim, xstim)
# plot_watermark(fig_extra, os.path.basename(__file__), filename, settings.git_branch, settings.git_short_hash)
# print("[+] Saving figure 'figures/%s'" %fig_name)
# fig_extra.savefig(os.path.join(dirs['figures'], fig_name))
#
# # newer version
# fig_extra, extra_axs, fig_name = plot_network_output2(spike_mon_E_all[-1][0], spike_mon_I_all[-1][0], state_mon_s2r, state_mon_order_param, tv_stim, xstim)
# plot_watermark(fig_extra, os.path.basename(__file__), filename, settings.git_branch, settings.git_short_hash)
# print("[+] Saving figure 'figures/%s'" %fig_name)
# fig_extra.savefig(os.path.join(dirs['figures'], fig_name))
# tight_layout()
# show()
# Save the results as .txt files (rows: time | cols: data)
# -------------------------------------------------------------#
print('\n[92] Saving results...')
# if not using fixed input
if not settings.fixed_input_enabled:
# Kuramoto monitors
print("[+] Saving Kuramoto monitor data")
# np.savetxt(os.path.join(dirs['datas'], 'state_mon_kuramoto_Theta.txt'), state_mon_kuramoto.Theta.T, fmt='%.8f')
np.savetxt(os.path.join(dirs['data'], 'order_param_mon_phase.txt'), state_mon_order_param.phase.T, fmt='%.8f')
np.savetxt(os.path.join(dirs['data'], 'order_param_mon_rhythm.txt'), state_mon_order_param.rhythm.T/nA, fmt='%.8f')
np.savetxt(os.path.join(dirs['data'], 'order_param_mon_coherence.txt'), state_mon_order_param.coherence.T, fmt='%.8f')
# CA1 firing rate
print("[+] Saving CA1 firing rate")
np.savetxt(os.path.join(dirs['data'], 'rate_mon_E_CA1.txt'), rate_mon_E_all[3][0].smooth_rate(window='gaussian', width=50*ms).T/Hz, fmt='%.8f')
np.savetxt(os.path.join(dirs['data'], 's2r_mon_drive.txt'), state_mon_s2r.drive_.T, fmt='%.8f')
# External stimulus
print("[+] Saving external stimulus")
np.savetxt(os.path.join(dirs['data'], 'stim_input.txt'), xstim, fmt='%.2f')