# BindsNET/bindsnet

Simulation of spiking neural networks (SNNs) using PyTorch.

Repository: https://github.com/BindsNET/bindsnet
Canonical: https://ross.abutalabs.com/products/bindsnet
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: spiking-neural-networks, gpu-computing, pytorch, machine-learning, snn, reinforcement-learning, stdp, dynamic, neurons, simulation, synapse
Last push: 2026-08-20T15:19:52+00:00

## Health v2 (maintenance only)
Score: 84/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 98, release rhythm 56, longevity 100
- inputs: {"age_days": 3127, "days_push": 13, "days_rel": 80, "gap_med": 604, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1695, forks 347 (observed 2026-08-28T04:05:23.849042+00:00)

## What it is
BindsNET is a Python package for simulating spiking neural networks (SNNs) built on PyTorch tensor functionality, running on CPUs or GPUs. It targets research on biologically inspired machine learning algorithms, including STDP-based learning and reinforcement learning applications.

## Use cases
- simulate spiking neural networks in Python
- train SNNs with STDP learning rules
- run neuromorphic-style experiments on GPU with PyTorch
- apply spiking networks to reinforcement learning environments
- prototype biologically inspired neural network algorithms
- convert datasets into spike encodings for SNN experiments

## When to choose
- you need a PyTorch-based SNN simulator with GPU acceleration
- you are researching biologically plausible learning rules like STDP
- you want to experiment with spiking networks on RL tasks
- you need a flexible, code-first SNN framework for research

## When to avoid
- you need production deep learning with conventional ANNs
- you want a high-level GUI or no-code SNN tool
- you target neuromorphic hardware deployment rather than simulation
- you need a stable, long-term-supported production library

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, deep-learning, reinforcement-learning, gpu-computing
- domain: machine-learning, deep-learning, simulation
- platform: python, cross-platform
- tags: spiking-neural-networks, snn, pytorch, neuromorphic, stdp, biologically-inspired, neuroscience, gpu

## Member repositories
- BindsNET/bindsnet (main) score 84

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:23.849042+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:55.253968+00:00, confidence not recorded.
  - readme: https://github.com/BindsNET/bindsnet (fetched 2026-08-28T04:05:23.849042+00:00, sha 0b43119637c0)
- Data as of 2026-08-30T08:39:29.467469+00:00.