Neuron

Digital Brain Simulation

⚠️ LICENSE & USAGE NOTICE — READ FIRST

This repository is source-available for private technical evaluation and testing only.

• ❌ No commercial use
• ❌ No production use
• ❌ No academic, institutional, or government use
• ❌ No research, benchmarking, or publication
• ❌ No redistribution, sublicensing, or derivative works
• ❌ No independent development based on this code

All rights remain exclusively with the author.
Use of this software constitutes acceptance of the terms defined in LICENSE.txt.

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A high-fidelity neural simulation framework designed to model biological brain activity with 85-90%+ accuracy. Neuron implements a versatile GeneralizedNeuron model capable of simulating diverse neural types and regional behaviors (Cortex, Cerebellum, Brainstem).

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🚀 Key Features

• Multi-Type Neuron Modeling: Supports Pyramidal, Interneuron, Sensory, Purkinje, Granule, and Motor neurons.
• Biologically Inspired Dynamics:
  • Spiking & Refractory Periods: Realistic membrane potential resets and recovery times.
  • STDP (Spike-Timing-Dependent Plasticity): Hebbian learning that modifies synaptic weights based on millisecond-level spike timing.
  • Spike-Frequency Adaptation: Threshold adjustments based on input variance.
  • Dendritic Processing: Integration of proximal and distal inputs with signal attenuation.
• Regional Specialization: Behavior varies by brain region (e.g., Purkinje cells in the Cerebellum vs. Pyramidal cells in the Cortex).
• Recurrent Network Simulation: A framework for building connected populations of neurons with both external and recurrent connectivity.

🛠 Project Structure

• neuron.py: The core GeneralizedNeuron class containing the mathematical models for membrane dynamics and learning.
• network.py: A simulation environment for connecting multiple neurons into a functioning network.
• visualize_neuron.py: Diagnostic tool for plotting single-neuron membrane potential, spikes, adaptation current, and STDP weight changes.
• visualize_network.py: Visualization of population dynamics and spike counts across the network.

🔧 Installation & Setup

1. Clone the repository:

git clone [neuron.git](https://github.com/7h3v01d/neuron.git)

   cd neuron

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

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📈 Usage

Run a Single Neuron Simulation To see how a Pyramidal neuron responds to sustained input and how its weights adapt via STDP:

python -m src.visualize_neuron

Run Network Simulation To observe the interaction between excitatory and inhibitory neurons in a recurrent loop:

python -m src.visualize_network

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🔬 Research Objectives

This project aims to replicate specific biological benchmarks:

• Inhibitory Balance: Targeting ~20 inhibitory spikes for interneurons (Neuron 9) over a 40-step cycle.
• Rapid Response: Optimizing Neurons 2, 3, and 5 for early-onset spiking (within 4-5 steps).
• Regional Accuracy: Modeling the unique thresholding and scaling of brainstem motor neurons and cerebellar purkinje cells.

Inspired by research into digital brain architectures and human scientific discovery.

Contribution Policy

Feedback, bug reports, and suggestions are welcome.

You may submit:

• Issues
• Design feedback
• Pull requests for review

However:

• Contributions do not grant any license or ownership rights
• The author retains full discretion over acceptance and future use
• Contributors receive no rights to reuse, redistribute, or derive from this code

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License

This project is not open-source.

It is licensed under a private evaluation-only license. See LICENSE.txt for full terms.