BrainPy Learning Notes

A small computational-neuroscience learning repository for experimenting with BrainPy-style modeling workflows and reproducible neuron-model simulations.

What it demonstrates

• Interest in neuroscience, dynamical systems, and brain-inspired computation
• Notebook-based exploration of Python scientific-computing workflows
• A clean, dependency-light spiking-neuron demo that runs from a fresh environment
• Early-stage research/project organization for computational neuroscience learning

Contents

• izhikevich_demo.py — minimal reproducible simulation of a regular-spiking Izhikevich neuron
• outputs/izhikevich_voltage_trace.png — generated voltage trace from the demo
• examples.ipynb — exploratory notebook for BrainPy/modeling notes
• requirements.txt — lightweight environment for the reproducible demo

Quick start

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python izhikevich_demo.py

Expected output:

Simulated 1000 time steps
Spike count: <number of spikes>
Saved plot: outputs/izhikevich_voltage_trace.png

Why this model?

The Izhikevich neuron model is a compact dynamical-systems model that can produce biologically plausible spiking patterns with low computational cost. It is useful as a first bridge between mathematical modeling, neuroscience intuition, and Python simulation workflows.

Current limitations

This repo is still a learning artifact, not a finished research package. The notebook may contain older exploratory cells, while izhikevich_demo.py is the clean reproducible entry point.

Next improvements

• Add a BrainPy-native version of the same simulation
• Compare regular-spiking, fast-spiking, and bursting parameter sets
• Add notebook explanations of the differential equations
• Add tests for deterministic simulation output