Description of problem
I am monitoring the firing rate results of each cell in a network connected with CTX, FSN, GPe, MSN, and STN. However, when I increase the number of neurons in each cell by 10 times, the simulation results show that all neurons are firing at every time point. What could be the cause of this issue?
Minimal code to reproduce problem
FSN model code
from brian2 import *
eqs = ‘’’
dv/dt = (-g_L * (v - E_L) + g_L * Delta_T * exp((v - vt) / Delta_T) - u + I) / C : volt
du/dt = (a * (v - E_L) - u) / tau_w : amp
I = Ispon + Istim + Isyn : amp
Istim : amp
Ispon : amp
Isyn = -(I_AMPA_GPeT1 + I_NMDA + I_GABA_GPeT1): amp
I_AMPA_GPeT1 : amp
I_NMDA : amp
I_GABA_GPeT1 : amp
g_L : siemens
E_L : volt
Delta_T: volt
vt : volt
vr : volt
tau_w : second
th : volt
a : siemens
d : amp
C : farad
‘’’
class NeuronModel:
def init(self, N, params):
# Parse the parameters from the params dictionary
super().init(N, params)
self.neurons = None
def create_neurons(self):
raise NotImplementedError("Subclasses should implement this method.")
class GPeT1(NeuronModel):
def init(self, N, params):
# Parse the parameters from the params dictionary
self.N = N
self.params = params
self.neurons = None
def create_neurons(self):
# Define the GPe neuron model based on the params
self.neurons = NeuronGroup(self.N, eqs, threshold='v > th', reset='v = vr; u += d', method='euler')
self.neurons.g_L = self.params['g_L']['value'] * eval(self.params['g_L']['unit'])
self.neurons.E_L = self.params['E_L']['value'] * eval(self.params['E_L']['unit'])
self.neurons.Delta_T = self.params['Delta_T']['value'] * eval(self.params['Delta_T']['unit'])
self.neurons.vt = self.params['vt']['value'] * eval(self.params['vt']['unit'])
self.neurons.vr = self.params['vr']['value'] * eval(self.params['vr']['unit'])
self.neurons.tau_w = self.params['tau_w']['value'] * eval(self.params['tau_w']['unit'])
self.neurons.th = self.params['th']['value'] * eval(self.params['th']['unit'])
self.neurons.a = self.params['a']['value'] * eval(self.params['a']['unit'])
self.neurons.d = self.params['d']['value'] * eval(self.params['d']['unit'])
self.neurons.C = self.params['C']['value'] * eval(self.params['C']['unit'])
return self.neurons
