From b021342b8e27f4a32d0ef1c731255ec589eb2a52 Mon Sep 17 00:00:00 2001
From: Jannis <jannis.schuecker@iais.fraunhofer.de>
Date: Thu, 7 Jun 2018 15:01:33 +0200
Subject: [PATCH] work on 1D network class for Figure2
---
.../SchueckerSchmidt2017/Fig2_EE_network.py | 39 +++++++++++++------
1 file changed, 28 insertions(+), 11 deletions(-)
diff --git a/figures/SchueckerSchmidt2017/Fig2_EE_network.py b/figures/SchueckerSchmidt2017/Fig2_EE_network.py
index de51237..994974a 100644
--- a/figures/SchueckerSchmidt2017/Fig2_EE_network.py
+++ b/figures/SchueckerSchmidt2017/Fig2_EE_network.py
@@ -8,13 +8,14 @@ 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
class network1D:
def __init__(self, params):
self.label = '1D'
self.params = {'input_params': params['input_params'],
- 'neuron_params': {'single_neuron_dict': single_neuron_dict},
+ 'neuron_params': {'single_neuron_dict': copy(single_neuron_dict)},
'connection_params': {'replace_cc': None,
'replace_cc_input_source': None}
}
@@ -23,23 +24,38 @@ class network1D:
self.structure = {'A': {'E'}}
self.structure_vec = ['A-E']
self.area_list = ['A']
- self.K_matrix = np.array([[params['K'], params['K']]])
+ if 'K_stable' in params.keys():
+ self.K_matrix = np.array([[params['K_stable'], params['K']]])
+ else:
+ 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, {})
-
+
def Phi(self, rate):
mu, sigma = self.theory.mu_sigma(rate)
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'],
+ 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))
+ def Phi_noisefree(self, rate):
+ mu, sigma = self.theory.mu_sigma(rate)
+ NP = self.params['neuron_params']['single_neuron_dict']
+ th_shift = NP['V_th'] - NP['E_L']
+ if mu > th_shift:
+ T = 1e-3 * NP['tau_m'] * \
+ np.log(mu[0] / (mu[0] - th_shift))
+ return (1 / T)
+ else:
+ return 0.
+
def fsolve(self, rates_init):
def f(rate):
return self.Phi(rate) - rate
@@ -63,19 +79,20 @@ class network2D:
self.structure = {'A': {'E1', 'E2'}}
self.structure_vec = ['A-E1', 'A-E2']
self.area_list = ['A']
- self.K_matrix = np.array([[params['K'] / 2., params['K'] / 2., params['K']]])
+ self.K_matrix = np.array(
+ [[params['K'] / 2., params['K'] / 2., params['K']]])
self.W_matrix = np.array([[params['W'], params['W'], params['W']]])
self.J_matrix = convert_syn_weight(self.W_matrix,
self.params['neuron_params']['single_neuron_dict'])
self.theory = Theory(self, {})
-
+
def Phi(self, rate):
mu, sigma = self.theory.mu_sigma(rate)
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'],
+ 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))
--
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