https://github.com/mohazamani/snn-visual-cortex-simulation

Simulates image processing in the visual cortex using Gabor and DoG filters in spiking neural networks.
https://github.com/mohazamani/snn-visual-cortex-simulation

computational-neuroscience computational-vision dog-filter gabor-filter neural-coding neural-networks neuroscience primary-visual-cortex spiking-neural-networks ttfs vision-neuroscience

Last synced: 3 days ago
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Simulates image processing in the visual cortex using Gabor and DoG filters in spiking neural networks.

• Host: GitHub
• URL: https://github.com/mohazamani/snn-visual-cortex-simulation
• Owner: MohaZamani
• Created: 2024-10-17T07:32:55.000Z (28 days ago)
• Default Branch: main
• Last Pushed: 2024-10-17T13:01:17.000Z (28 days ago)
• Last Synced: 2024-10-17T23:23:17.484Z (27 days ago)
• Topics: computational-neuroscience, computational-vision, dog-filter, gabor-filter, neural-coding, neural-networks, neuroscience, primary-visual-cortex, spiking-neural-networks, ttfs, vision-neuroscience
• Language: Jupyter Notebook
• Homepage:
• Size: 18 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Processing in Primary Visual Cortex and Interlayer Communication in Spiking Neural Networks



  



This project focuses on simulating image processing mechanisms inspired by the **primary visual cortex (V1)** and investigating **interlayer communication** in **Spiking Neural Networks (SNNs)**. The project implements filters like **Difference of Gaussians (DoG)** and **Gabor filters** to simulate the functionality of V1 neurons, and uses neural coding schemes such as **Time to First Spike (TTFS)** and **Poisson coding**. The **Spike-Timing-Dependent Plasticity (STDP)** learning rule is applied to enhance the learning process in SNNs.

## Table of Contents

- [Project Overview](#project-overview)

- [Implemented Features](#implemented-features)

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

- [Results](#results)

- [References](#references)

## Project Overview

This project aims to simulate the functionality of visual cortex neurons through the application of **DoG** and **Gabor filters** on grayscale images, mimicking edge detection processes in biological vision systems. Furthermore, the project explores the encoding of visual information using **TTFS** and **Poisson coding**, and analyzes the performance of **spiking neural networks** with interlayer communication using the **STDP learning rule**.

## Implemented Features

1. **DoG Filter**:

   - Simulates on-center off-surround and off-center on-surround retinal receptive fields for edge detection.



  



2. **Gabor Filter**:

   - Simulates simple cells in the primary visual cortex (V1), detecting edges at specific orientations and spatial frequencies.



  



3. **Neural Coding**:

   - **Time to First Spike (TTFS)**: Encodes images based on the timing of neuron spikes.

   - **Poisson Coding**: Encodes images using Poisson-distributed spike times.



  



4. **Spike-Timing-Dependent Plasticity (STDP)**:

   - STDP learning rule adjusts synaptic weights based on spike timing to optimize the SNN’s performance in recognizing visual patterns.



  



## How to Run

1. Clone the repository:

   ```bash

   git clone https://github.com/MohaZamani/SNN-Visual-Cortex-Simulation.git

2. Install the necessary dependencies:

   ```bash

   pip install -r requirements.txt

3. Run the simulation notebooks:

   - **For DoG and Gabor Filters**: Open and run `Filters.ipynb`

   - **For SNN with STDP**: Open and run `SNN.ipynb`

   You can launch the notebooks by executing:

   ```bash

   jupyter notebook

## Results

Results from the simulations include:

- **Edge Detection**: Visualization of the effects of DoG and Gabor filters on input images, showing enhanced edge detection.

- **Spike Raster Plots**: Visualization of neural activity using TTFS and Poisson coding.

- **Weight Changes**: Visualization of synaptic weight adjustments using the STDP learning rule.

All simulation results and detailed analysis is provided in the [report](./Report/Report-P5.pdf).

## References

- **Gabor Filters**: [Gabor Filters in Visual Processing](https://en.wikipedia.org/wiki/Gabor_filter)

- **STDP Learning**: [Spike-Timing-Dependent Plasticity on Wikipedia](https://en.wikipedia.org/wiki/Spike-timing-dependent_plasticity)

- **Primary Visual Cortex (V1)**: [Visual Cortex Overview](https://en.wikipedia.org/wiki/Visual_cortex)