brainpy.state provides comprehensive spiking neural network models built on JAX and brainstate. It is the point-neuron modeling layer of the BrainX ecosystem.

The library ships 167+ models organized in three tiers:

• Base classes: Dynamics, Neuron, Synapse, the abstract foundation every model inherits from.
• BrainPy-style models (45+): high-level, composable neurons (LIF, HH, Izhikevich, …), synapses (Expon, Alpha, AMPA, NMDA, …), projections, readouts, and input generators previously designed in BrainPy.
• NEST-compatible models (119+): faithful JAX re-implementations of NEST simulator neuron, synapse, plasticity (STDP, STP), and device models.
• All parameters carry physical units via brainunit, and every neuron supports surrogate-gradient-based training out of the box.

Compared to brainpy.dyn, brainpy.state has the following characteristics:

• Ecosystem compatability: brainpy.state is built on brainstate and fully compatible with BrainX ecosystem.
• Model scope: brainpy.state implements much more models including BrainPy-style models plus a large NEST-compatible model set.
• Scientific ergonomics: brainpy.state uses physical units via brainunit by default and is designed for surrogate-gradient training.

Features

• Comprehensive model library — 18 neuron families, 6 synapse types, 9 STDP rules, 17 generators, and more.
• Physical units everywhere — parameters use brainunit quantities (mV, ms, nS, …), preventing unit errors.
• Differentiable — surrogate gradients enable backpropagation through spiking networks for training with gradient descent.
• NEST compatibility — benchmarked against NEST for numerical accuracy; uses NEST-compatible parameter names.
• Hardware-accelerated — JAX backend with JIT compilation for CPU, GPU, and TPU.
• Composable architecture — mix-and-match neurons, synapses, synaptic outputs (COBA/CUBA/MgBlock), and projections.

Quick Example

import brainpy
import brainstate
import brainunit as u

# Create neuron populations
E = brainpy.state.LIF(3200, V_rest=-60*u.mV, V_th=-50*u.mV, tau=20*u.ms)
I = brainpy.state.LIF(800,  V_rest=-60*u.mV, V_th=-50*u.mV, tau=20*u.ms)

Links

• Documentation: https://brainpy-state.readthedocs.io/
• Source: https://github.com/chaobrain/brainpy.state
• Bug reports: https://github.com/chaobrain/brainpy.state/issues
• Ecosystem: https://brainmodeling.readthedocs.io/

Installation

brainpy.state requires Python >= 3.10 and runs on Linux, macOS, and Windows.

pip install brainpy.state -U

For hardware-specific JAX backends:

pip install brainpy.state[cpu] -U     # CPU only
pip install brainpy.state[cuda12] -U  # CUDA 12.x
pip install brainpy.state[cuda13] -U  # CUDA 13.x
pip install brainpy.state[tpu] -U     # TPU

Or install the full BrainX ecosystem:

pip install BrainX -U

Ecosystem

brainpy.state is one part of the BrainX ecosystem:

Package	Description
brainstate	State management for JAX-based brain modeling
brainunit	Physical units for neuroscience
brainevent	Event-driven sparse operators
braintools	Surrogate gradients, analysis, and utilities

Citing

If you use brainpy.state, please consider citing the following paper:

@article {10.7554/eLife.86365,
    article_type = {journal},
    title = {BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming},
    author = {Wang, Chaoming and Zhang, Tianqiu and Chen, Xiaoyu and He, Sichao and Li, Shangyang and Wu, Si},
    editor = {Stimberg, Marcel},
    volume = 12,
    year = 2023,
    month = {dec},
    pub_date = {2023-12-22},
    pages = {e86365},
    citation = {eLife 2023;12:e86365},
    doi = {10.7554/eLife.86365},
    url = {https://doi.org/10.7554/eLife.86365},
    abstract = {Elucidating the intricate neural mechanisms underlying brain functions requires integrative brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose programming framework that allows users to freely define neural models across multiple scales, efficiently simulate, train, and analyze model dynamics, and conveniently incorporate new modeling approaches. In response to this need, we present BrainPy. BrainPy leverages the advanced just-in-time (JIT) compilation capabilities of JAX and XLA to provide a powerful infrastructure tailored for brain dynamics programming. It offers an integrated platform for building, simulating, training, and analyzing brain dynamics models. Models defined in BrainPy can be JIT compiled into binary instructions for various devices, including Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Tensor Processing Unit (TPU), which ensures high running performance comparable to native C or CUDA. Additionally, BrainPy features an extensible architecture that allows for easy expansion of new infrastructure, utilities, and machine-learning approaches. This flexibility enables researchers to incorporate cutting-edge techniques and adapt the framework to their specific needs},
    journal = {eLife},
    issn = {2050-084X},
    publisher = {eLife Sciences Publications, Ltd},
}

@inproceedings{wang2024a,
    title={A differentiable brain simulator bridging brain simulation and brain-inspired computing},
    author={Chaoming Wang and Tianqiu Zhang and Sichao He and Hongyaoxing Gu and Shangyang Li and Si Wu},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=AU2gS9ut61}
}