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https://github.com/mohazamani/neural-learning-and-coding
Explores neural coding schemes and learning mechanisms in neuroscience using STDP and RSTDP models for synaptic plasticity.
https://github.com/mohazamani/neural-learning-and-coding
computatio computational-neuroscience encoding learning neural-networks neuroscience poisson spikin stdp stdp-learning ttfs
Last synced: 3 days ago
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Explores neural coding schemes and learning mechanisms in neuroscience using STDP and RSTDP models for synaptic plasticity.
- Host: GitHub
- URL: https://github.com/mohazamani/neural-learning-and-coding
- Owner: MohaZamani
- Created: 2024-05-22T07:48:21.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-10-17T07:58:11.000Z (28 days ago)
- Last Synced: 2024-10-19T09:24:49.522Z (26 days ago)
- Topics: computatio, computational-neuroscience, encoding, learning, neural-networks, neuroscience, poisson, spikin, stdp, stdp-learning, ttfs
- Language: Jupyter Notebook
- Homepage:
- Size: 12.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Neural Coding and Learning Mechanisms in Neuroscience
This project focuses on various coding schemes and learning methods in neural networks, including **STDP** (Spike-Timing Dependent Plasticity) for unsupervised learning and **RSTDP** (Reward-modulated STDP) for reinforcement learning. It implements models for encoding spike-based information and adjusting synaptic weights in response to input stimuli.
## Table of Contents
- [Project Overview](#project-overview)
- [Implemented Features](#implemented-features)
- [How to Run](#how-to-run)
- [Results](#results)
- [References](#references)
## Project Overview
The aim of this project is to explore how different neural coding schemes and learning rules can be applied in neuronal networks. It covers spike-based coding strategies such as **Time To First Spike (TTFS)** and **Poisson Distribution Encoding**, as well as learning mechanisms like **STDP** and **RSTDP** for adjusting synaptic weights based on temporal relationships between spikes and rewards.
## Implemented Features
1. **Neural Coding Schemes**:
- **Time To First Spike (TTFS)**: A method of encoding information based on the time of the first spike.
- **Poisson Distribution Encoding**: Simulating neuron spiking based on Poisson-distributed input spikes.
2. **Unsupervised Learning with STDP**:
- **Spike-Timing Dependent Plasticity (STDP)**: Adjusts synaptic weights based on the temporal order of pre- and post-synaptic spikes.
3. **Reinforcement Learning with RSTDP**:
- **Reward-modulated STDP (RSTDP)**: Synaptic plasticity is modulated by external rewards, where rewards reinforce useful patterns of activity.
4. **Synaptic Weight Dynamics**:
- Tracks changes in synaptic weights under different input conditions (noisy fixed input, Poisson-distributed inputs).
5. **Cosine Similarity**:
- Measures the similarity between synaptic weight vectors over time, showing the clustering of neurons in response to inputs.
6. **Inactive Neuron Impact**:
- Analyzes how inactive neurons (neurons that do not spike) affect the overall synaptic weight dynamics.
## How to Run
1. Clone the repository:
```bash
git clone https://github.com/MohaZamani/Neural-Learning-and-Coding.git
2. Install the necessary dependencies:
```bash
pip install -r requirements.txt
3. Run the simulation notebooks:
- **For Encoding**: Open and run `Coding.ipynb`
- **For STDP Learning**: Open and run `STDPLearning.ipynb`
- **For RSTDP Learning**: Open and run `RSTDPLearning.ipynb`
You can launch the notebooks using Jupyter by executing:
```bash
jupyter notebook
## Results
Results from the simulations include:
- **Spike Raster Plots**: Visualizing the spiking activity of neurons under different input coding strategies.
- **Synaptic Weight Changes**: Charts showing how synaptic weights evolve over time in response to different input conditions.
- **Cosine Similarity**: Plots measuring the similarity between synaptic weight vectors over time, demonstrating clustering and separation.
- **Impact of Inactive Neurons**: Analysis of how inactive neurons affect synaptic dynamics and learning processes.
All simulation results and further analysis is available in the [report](./Report/Report-p03.pdf).
## References
- **Poisson Coding**: [Wikipedia Article on Poisson Distribution](https://en.wikipedia.org/wiki/Poisson_distribution)
- **Hebbian learning**: [Hebb Rule and Experiments](https://neuronaldynamics.epfl.ch/online/Ch19.S1.html)
- **Spike-Timing Dependent Plasticity (STDP)**: [Wikipedia Article on STDP](https://en.wikipedia.org/wiki/Spike-timing-dependent_plasticity)
- **Reward-modulated STDP (RSTDP)**: [Article on R-STDP](https://ieeexplore.ieee.org/document/8460482)
- **Neural Dynamics**: [Neural Dynamics Online Resources](https://neuronaldynamics.epfl.ch)
- **Cosine Similarity**: [Wikipedia Article on Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)