https://github.com/mohazamani/neuronal-populations-simulation

Simulating Synaptic Dynamics and Decision-Making in Neuronal Populations
https://github.com/mohazamani/neuronal-populations-simulation

computational-neuroscience decision-ma excitatory-inhibitory-net neural-network neuron-model neuroscience spiking-neural-networks synapse

Last synced: about 2 months ago
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Simulating Synaptic Dynamics and Decision-Making in Neuronal Populations

• Host: GitHub
• URL: https://github.com/mohazamani/neuronal-populations-simulation
• Owner: MohaZamani
• Created: 2024-05-22T07:43:33.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-10-17T07:56:35.000Z (3 months ago)
• Last Synced: 2024-10-20T05:03:09.978Z (3 months ago)
• Topics: computational-neuroscience, decision-ma, excitatory-inhibitory-net, neural-network, neuron-model, neuroscience, spiking-neural-networks, synapse
• Language: Jupyter Notebook
• Homepage:
• Size: 15.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Simulating Synaptic Dynamics and Decision-Making in Neuronal Populations

This project implements simulations of synaptic interactions between neuron populations, focusing on the mechanisms of synaptic transmission and neuronal decision-making. It models excitatory and inhibitory neuron populations, explores different connectivity patterns, and investigates neuron responses to noisy and non-noisy inputs using various synaptic dynamics.

## Table of Contents

- [Project Overview](#project-overview)

- [Implemented Features](#implemented-features)

- [Simulation Inputs](#simulation-inputs)

- [How to Run](#how-to-run)

- [Results](#results)

- [References](#references)

## Project Overview

This project is part of the second neural computation course project. The main goals are to understand how synaptic mechanisms work, analyze the behavior of neuron populations under different stimulation conditions, and simulate decision-making processes in neural circuits.

## Implemented Features

1. **Synaptic Mechanisms**: 

   - Implementation of synapses using the **Dirac Delta function** to model spike timing and transmission.

   - Comparison of dynamic synapses based on conductance.

2. **Neuronal Populations**: 

   - Two distinct neuron populations: **Excitatory** (80%) and **Inhibitory** (20%) neurons, modeled with different parameters.

   - Connectivity between neurons using various strategies such as **full connectivity**, **fixed coupling probability**, and **fixed number of presynaptic partners**.

3. **Noisy Inputs**: 

   - Simulation of neuron population responses to both **noisy** and **non-noisy** input currents, analyzing the sensitivity and firing rates under different conditions.

4. **Decision-Making Simulation**: 

   - Simulation of decision-making processes when two neuron populations receive inputs, demonstrating competition and activity dynamics between excitatory and inhibitory neurons.



  



## Simulation Inputs

The project investigates several types of inputs, including:

- **Constant Input**

- **Noisy Input**

- **Step Input**

- **Random Input Currents**

## How to Run

1. Clone the repository:

   ```bash

   git clone https://github.com/MohaZamani/Neuronal-Populations-Simulation.git

2. Install the necessary dependencies:

   ```bash

   pip install -r requirements.txt

3. Run the simulation notebooks:

   - Open and run `main.ipynb`

   Launch the notebooks by executing:

   ```bash

   jupyter notebook

## Results

Results from the simulations, including raster plots of neuronal activity, connectivity graphs, and decision-making dynamics, can be found in the [report](./Report/Report.pdf).

## References

- [PymoNNtorch Framework]( https://github.com/cnrl/PymoNNtorch)

- [Neural Dynamics](https://neuronaldynamics.epfl.ch)

- Dirac Delta Function: [Wikipedia Article](https://en.wikipedia.org/wiki/Dirac_delta_function)