This directory contains example scripts demonstrating the capabilities of BrainPy 2.x for brain dynamics programming.

These examples showcase BrainPy 2.x functionality including dynamics simulation, analysis, and training. BrainPy 2.x is maintained for backward compatibility, but new projects should consider using BrainPy 3.x.

As of September 2025, BrainPy has been upgraded to version 3.x. To use these examples with BrainPy 3.x, update your imports:

import brainpy as bp
import brainpy.math as bm

Network simulation examples demonstrating various neural models and dynamics:

• hh_model.py - Hodgkin-Huxley neuron model
• ei_nets.py - Excitatory-inhibitory networks
• COBA.py - Conductance-based network model
• stdp.py - Spike-timing-dependent plasticity
• decision_making_network.py - Decision-making circuit
• whole_brain_simulation_with_fhn.py - Whole-brain simulation with FitzHugh-Nagumo model
• whole_brain_simulation_with_sl_oscillator.py - Whole-brain simulation with Stuart-Landau oscillator

Phase plane and bifurcation analysis of neural models:

• 1d_qif.py - 1D Quadratic Integrate-and-Fire model analysis
• 2d_fitzhugh_nagumo_model.py - 2D FitzHugh-Nagumo phase plane analysis
• 2d_mean_field_QIF.py - 2D mean-field QIF analysis
• 3d_reduced_trn_model.py - 3D reduced thalamic reticular nucleus model
• 4d_HH_model.py - 4D Hodgkin-Huxley model analysis
• highdim_RNN_Analysis.py - High-dimensional RNN dynamics analysis

Training examples for recurrent networks and reservoir computing:

• echo_state_network.py - Echo State Network (reservoir computing)
• integrator_rnn.py - RNN for integration task
• reservoir-mnist.py - MNIST classification with reservoir computing
• Sussillo_Abbott_2009_FORCE_Learning.py - FORCE learning algorithm
• Song_2016_EI_RNN.py - E/I RNN training
• integrate_brainpy_into_flax-lif.py - Integration with Flax (LIF neurons)
• integrate_brainpy_into_flax-convlstm.py - Integration with Flax (ConvLSTM)
• integrate_flax_into_brainpy.py - Using Flax models in BrainPy

Artificial neural network training examples:

• mnist-cnn.py - CNN for MNIST classification
• mnist_ResNet.py - ResNet for MNIST classification

Spiking neural network training examples:

• spikebased_bp_for_cifar10.py - Spike-based backpropagation for CIFAR-10
• readme.md - Additional SNN training documentation

pip install -U brainpy[cpu]  # or brainpy[cuda12] for GPU

For version 3.x with 2.x compatibility:

pip install -U brainpy[cpu]
# Then use: import brainpy as bp

Run any example directly:

python dynamics_simulation/hh_model.py

Or with version 3.x (examples may need import updates):

# Modify imports in the script first, then run
python dynamics_simulation/ei_nets.py

• Dynamics Simulation: Simulating neural circuits and network dynamics
• Dynamics Analysis: Phase plane analysis, bifurcation analysis, fixed points
• Reservoir Computing: Echo State Networks and Liquid State Machines
• Network Training: Gradient-based and FORCE learning for RNNs
• SNN Training: Surrogate gradient methods for spiking networks
• Framework Integration: Using BrainPy with other frameworks (Flax, JAX)

• BrainPy 2.x Documentation
• BrainPy 3.x Documentation
• BrainPy Ecosystem

For new projects, we recommend using BrainPy 3.x which offers improved performance and a cleaner API. See the migration guide in the main documentation.

For questions and support, please visit the BrainPy GitHub repository.