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Commit 2d77708d authored by Maximilian Schmidt's avatar Maximilian Schmidt
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Formatting in Fig2 script, delete deprecated 1D Data script

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1 merge request!1Add all necessary files for the multi-area model
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figures/SchueckerSchmidt2017/Fig2_EE_example.py
+ 16
− 7
View file @ 2d77708d
......@@ -6,8 +6,6 @@ import pyx
from matplotlib import gridspec
from matplotlib.colors import ListedColormap
from plotcolors import myred, myblue
import pyx
import os
from Fig2_EE_network import network1D, network2D
"""
......@@ -52,8 +50,6 @@ pl.rcParams['ytick.labelsize'] = scale * 8
pl.rcParams['xtick.labelsize'] = scale * 8
# use latex to generate the labels in plots
# not needed anymore in newer versions
# using this, font detection fails on adobe illustrator 2010-07-20
pl.rcParams['text.usetex'] = True
pl.rcParams['text.latex.preamble'] = [r"\usepackage{amsmath}"]
......@@ -66,7 +62,6 @@ nrows = 2.
ncols = 2.
width = 4.61 # inches for 1.5 JoN columns
height = 5.67
print(width, height)
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
......@@ -124,6 +119,8 @@ for label in ['A', 'B', 'C', 'D', 'E', 'F', 'G']:
"""
1D network
"""
ax = axes['A']
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticks_position('none')
......@@ -146,6 +143,14 @@ ax_inset.tick_params(axis='y', labelsize=4, pad=1)
x = np.arange(0, 150., 1.)
"""
Panel C (top): Transfer function
for three cases:
- normal network
- noiseless neurons
- neurons without refractory period
"""
ax = axes['C']
ax.xaxis.set_ticks_position('none')
ax.spines['bottom'].set_color('none')
......@@ -189,6 +194,10 @@ ax.set_xlim([-3, 70])
ax.set_ylim([-3, 70])
"""
Panel C (bottom): Transfer function
for three levels of external stimulation.
"""
ax = axes['C2']
colors = ['k', '0.3', '0.7']
markers = ['d', '+', '.']
......@@ -413,7 +422,7 @@ y0 = 0.
x1 = 0.015
y1 = 0.015
# vector fiels
# vector fields
netp = network_params_base
net = network2D(netp)
range_vector = np.arange(0., 51., 10.)
......@@ -440,7 +449,7 @@ nullcline_x0 = net.nullclines_x0(x0_vec)
ax.plot(nullcline_x0, x0_vec, '--', color='black', label='x0')
ax.plot(x0_vec, nullcline_x0, '--', color='black', label='x0')
# set limes
# set plot limits
axG.set_xlim((x0 * 1e2, x1 * 1e2))
axG.set_ylim((y0 * 1e2, y1 * 1e2))
ax.set_xlim([-5, 50])
......
figures/SchueckerSchmidt2017/Fig2_EE_example_1D_data.py deleted 100644 → 0
+ 0
− 194
View file @ 186fecce
import os
import pylab as pl
import numpy as np
from plotcolors import myred, myblue
from matplotlib.colors import ListedColormap
from multiarea_model.theory import Theory
from multiarea_model.theory_helpers import nu0_fb
from multiarea_model.default_params import single_neuron_dict
from multiarea_model.multiarea_helpers import convert_syn_weight
class network:
def __init__(self, params, theory_spec):
self.label = '1D'
self.params = {'input_params': params['input_params'],
'neuron_params': {'single_neuron_dict': single_neuron_dict},
'connection_params': {'replace_cc': None,
'replace_cc_input_source': None}
}
self.add_DC_drive = np.zeros(1)
self.structure = {'A': {'E'}}
self.structure_vec = ['A-E']
self.area_list = ['A']
self.K_matrix = np.array([[params['K'], params['K']]])
self.W_matrix = np.array([[params['W'], params['W']]])
self.J_matrix = convert_syn_weight(self.W_matrix,
self.params['neuron_params']['single_neuron_dict'])
self.theory = Theory(self, theory_spec)
def Phi(self, rate):
mu, sigma = self.theory.mu_sigma(rate)
# print(mu, sigma)
NP = self.params['neuron_params']['single_neuron_dict']
return list(map(lambda mu, sigma: nu0_fb(mu, sigma,
1.e-3*NP['tau_m'],
1.e-3*NP['tau_syn_ex'],
1.e-3*NP['t_ref'],
NP['V_th'] - NP['E_L'],
NP['V_reset'] - NP['E_L']),
mu, sigma))
"""
space showing bifurcation
"""
rate_exts_array = np.arange(150., 170.1, 1.)
network_params = {'K': 210.,
'W': 10.}
theory_params = {'T': 20.,
'dt': 0.01}
# for i, rate_ext in enumerate(rate_exts_array):
# input_params = {'rate_ext': rate_ext}
# network_params.update({'input_params': input_params})
# net = network(network_params, theory_params)
# r = net.theory.integrate_siegert()[1][:, -1]
# print(r)
# x = np.arange(0, 30., 0.02)
# for i, rate_ext in enumerate([150., 160., 170.]):
# input_params = {'rate_ext': rate_ext}
# network_params.update({'input_params': input_params})
# net = network(network_params, theory_params)
# y = np.fromiter([net.Phi(xi) for xi in x], dtype=np.float)
# pl.plot(x, y)
# pl.savefig('Fig2_EE_example_1D_data.eps')
fig = pl.figure()
x = np.arange(0, 70., 1.)
for rate_ext in [150., 160., 170.]:
input_params = {'rate_ext': rate_ext}
network_params.update({'input_params': input_params})
net = network(network_params, theory_params)
y = np.fromiter([net.Phi(x[i])[0] for i in range(len(x))], dtype=np.float)
pl.plot(x, y)
pl.plot(x, x, '--')
pl.show()
# for i, dic in enumerate(mfp.par_list(PS)):
# print(dic)
# para_dic, label = mfp.hashtag(dic)
# mf = meanfield_multi(para_dic, label)
# inits = np.arange(0, 50, 10)
# Fps = []
# for init in inits:
# solution = mf.fsolve([init, init])
# if solution['eps'] == 'The solution converged.':
# Fps.append(mf.fsolve([init, init])['rates'][0])
# Fps = np.unique(np.round(Fps, decimals=2))
# Fps_array.append(Fps)
# h5.add_to_h5('mf_data.h5', {'EE_example': {
# 'Fps_array': Fps_array}}, 'a', overwrite_dataset=True)
# rate_exts_array = np.arange(150., 170.1, 10.)
# rate_exts = np.array([[x, x] for x in rate_exts_array])
# PS = para.ParameterSpace({ # 'g': para.ParameterRange(np.arange(-3.5,-3.0,0.1)),
# 'g': 1.,
# 'rate': para.ParameterRange(rate_exts),
# 'model': 'Brunel',
# 'gamma': 1.,
# 'rates_init_int': np.array([80, 80]),
# 'W': 0.01,
# 'K': 210.,
# })
# print(PS)
# cmap = pl.get_cmap('Greys_r')
# ################## rate instability ###############
# x = np.arange(0, 150, 1.0)
# x_long = np.arange(0, 10000, 10.0)
# NUM_COLORS = len(mfp.par_list(PS))
# for i, dic in enumerate(mfp.par_list(PS)):
# para_dic, label = mfp.hashtag(dic)
# mf = meanfield_multi(para_dic, label)
# dic_refrac = copy.deepcopy(dic)
# dic_refrac.update({'tau_refrac': 0})
# para_dic_refrac, label_refrac = mfp.hashtag(dic_refrac)
# mf_refrac = meanfield_multi(para_dic_refrac, label_refrac)
# t = [mf.Phi(np.array([xval, xval]), return_leak=False) for xval in x]
# t_noisefree = [mf.Phi_noisefree(np.array([xval, xval])) for xval in x]
# t_refrac = [mf_refrac.Phi(np.array([xval, xval]),
# return_leak=False) for xval in x]
# t_long = [mf.Phi(np.array([xval, xval]), return_leak=False)
# for xval in x_long]
# h5.add_to_h5('mf_data.h5', {'EE_example': {label: {'t': t, 't_noisefree': t_noisefree,
# 't_long': t_long, 't_refrac': t_refrac}}}, 'a', overwrite_dataset=True)
# ########################## Stabilization ################
# x = np.arange(0., 50., 0.1)
# rate = 160.
# start = 17.0
# drate = 1.
# dic = { # 'g': para.ParameterRange(np.arange(-3.5,-3.0,0.1)),
# 'g': 1.,
# 'rate': np.array([rate, rate]),
# 'model': 'Brunel',
# 'gamma': 1.,
# 'W': 0.01,
# 'K': 210.,
# }
# ######### base state ##########
# para_dic, mf_label = mfp.hashtag(dic)
# mf = meanfield_multi(para_dic, mf_label)
# res_la_base = mf.fsolve(np.ones(2) * start)
# res_in_base = mf.fsolve(np.ones(2) * start)
# print('res', res_la_base)
# t = [mf.Phi(np.array([xval, xval])) for xval in x]
# savedic = {'res_la_base': res_la_base, 'res_in_base': res_in_base, 't': t}
# h5.add_to_h5('mf_data.h5', {'EE_example': {
# mf_label: savedic}}, 'a', overwrite_dataset=True)
# ######## increased rate #######
# dic.update({'rate': np.array([rate + drate, rate + drate])})
# para_dic, mf_label = mfp.hashtag(dic)
# mf_2 = meanfield_multi(para_dic, mf_label)
# res_in_2 = mf_2.fsolve(np.ones(2) * start)
# t_2 = [mf_2.Phi(np.array([xval, xval])) for xval in x]
# savedic = {'res_in_2': res_in_2, 't_2': t_2}
# h5.add_to_h5('mf_data.h5', {'EE_example': {
# mf_label: savedic}}, 'a', overwrite_dataset=True)
# ####### stabilized #######
# matrix_prime, v, shift, delta = mft.stabilize(
# mf, mf_2, fixed_point=res_la_base['rates'][0], method='least_squares')
# print('1D K_prime', matrix_prime)
# dic.update({'K_stable': matrix_prime})
# para_dic, mf_label = mfp.hashtag(dic)
# mf_s = meanfield_multi(para_dic, mf_label)
# res_in_s = mf_s.fsolve(np.ones(2) * start)
# t_s = [mf_s.Phi(np.array([xval, xval])) for xval in x]
# savedic = {'res_in_s': res_in_s, 't_s': t_s}
# h5.add_to_h5('mf_data.h5', {'EE_example': {
# mf_label: savedic}}, 'a', overwrite_dataset=True)
figures/SchueckerSchmidt2017/Fig2_EE_network.py
+ 14
− 4
View file @ 2d77708d
import os
import pylab as pl
import numpy as np
from scipy import optimize
from plotcolors import myred, myblue
from matplotlib.colors import ListedColormap
from multiarea_model.theory import Theory
from multiarea_model.theory_helpers import nu0_fb
from multiarea_model.default_params import single_neuron_dict, nested_update
from multiarea_model.multiarea_helpers import convert_syn_weight
from copy import copy
"""
Network class for the 1D case:
1 excitatory population with recurrent connectivity and external
stimulation.
"""
class network1D:
def __init__(self, params):
......@@ -66,6 +69,13 @@ class network1D:
return result_dic
"""
Network class for the 2D case:
2 excitatory populations with recurrent connectivity and external
stimulation.
"""
class network2D:
def __init__(self, params):
self.label = '2D'
......
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