# Firing rate result

**URL:** <https://brian.discourse.group/t/firing-rate-result/1344>\
**Category:** Support\
**Created:** [6 December 2024 05:38 UTC](https://brian.discourse.group/t/firing-rate-result/1344 "2024-12-06T05:38:54Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![keun](https://avatars.discourse-cdn.com/v4/letter/k/3da27b/32.png) [@keun](https://brian.discourse.group/u/keun)\
**Post date:** [6 December 2024 05:38 UTC](https://brian.discourse.group/t/firing-rate-result/1344/1 "2024-12-06T05:38:54Z")

</div>

# 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

```

# What you have aready tried

 ![raster_plot_20241205_135447](https://global.discourse-cdn.com/free1/uploads/brian/original/1X/bbf524f6c07d6d8a95c7fc9d6687b9b1ef3a2bee.jpeg)

# Expected output (if relevant)

# Actual output (if relevant)

# Full traceback of error (if relevant)

---

<div class="post-metadata">

**Author:** ![mstimberg](https://yyz2.discourse-cdn.com/free1/user_avatar/brian.discourse.group/mstimberg/32/11_2.png) [@mstimberg](https://brian.discourse.group/u/mstimberg)\
**Post date:** [6 December 2024 21:00 UTC](https://brian.discourse.group/t/firing-rate-result/1344/2 "2024-12-06T21:00:00Z")

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Hi @keun, It is hard to see what is going on like this, but how did you scale the synaptic parameters of your network with the increased number of neurons? For example, if you have fixed connection probabilities and weights, then each neuron will receive 10 times more input after scaling up the network size.