Shikhargupta/Spiking-Neural-Network

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1210 stars · 297 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A pure Python implementation of a hardware-efficient spiking neural network (SNN) trained with Spike-Time Dependent Plasticity (STDP). It includes neuron, synapse, receptive field, and spike train components plus a winner-takes-all classification simulator demonstrated on MNIST.

Use cases

• simulate spiking neural networks in python
• learn STDP-based unsupervised weight learning
• classify MNIST digits with a spiking network
• prototype on-chip neuromorphic learning algorithms
• understand spike trains and receptive fields
• experiment with energy-efficient neural network learning

When to choose

• you want a simple, readable pure-Python SNN simulator for learning or teaching
• you are exploring STDP and neuromorphic computing concepts
• you need a lightweight educational baseline before using a full simulator like Brian2 or Norse

When to avoid

• you need high performance or GPU acceleration
• you require production-grade deep learning training pipelines
• you need actively maintained code with broad community support

Facets

library · maturity maintenance

machine-learning simulation machine-learning deep-learning python cross-platform spiking-neural-networks stdp neuromorphic-computing mnist pure-python neuromorphic

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