NeuroSimLib

librería para simular redes neuronales biológicas basadas en el modelo Integrate-and-Fire (LIF)

NeuroSimLib

Biological Neural Network Simulation: Integrate-and-Fire (LIF) Model

NeuroSimLib is a Python library for simulating biological neurons using the Leaky Integrate-and-Fire (LIF) model. It allows modeling basic neural networks with synaptic connections, advanced plasticity rules like STDP (Spike-Timing-Dependent Plasticity), and dynamic visualization of neural activity.

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🚀 Features

• LIF Neuron Simulation: Simulate single neurons using the classic Integrate-and-Fire model.
• Neural Networks: Create networks of multiple interconnected neurons.
• Synaptic Connections:
  • Fixed connections.
  • Synaptic plasticity rules, including STDP and Hebbian Learning.
• Dynamic Visualization:
  • Interactive real-time plots with Plotly.
  • Visualization of membrane potentials and spike times.

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📥 Installation

Clone the repository and install the library locally using pip:

📊 Visualization
Dynamic and interactive visualizations can be generated using Plotly:

Membrane potentials over time.
Synaptic weight evolution during learning.

🔧 Dependencies
Python 3.8+
NumPy
Matplotlib
Plotly
Install dependencies using:

bash
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pip install numpy matplotlib plotly

🧪 Examples
Explore the examples/ folder for detailed scripts:

single_neuron.py: Simulate a single LIF neuron.
multi_neuron_network.py: Simulate a network of interconnected neurons.
plasticity_demo.py: Demonstrates STDP plasticity.

📜 License
This project is licensed under the MIT License. See the LICENSE file for details.

🤝 Contributing
Contributions are welcome! If you'd like to add features, fix bugs, or improve documentation:

Fork the repository.
Create a new branch.
Submit a pull request.

🧠 About
NeuroSimLib was created to simulate basic biological neural networks and explore dynamic behaviors like spiking, plasticity, and learning.