A paper list of spiking neural networks, including papers, codes, and related websites. 本仓库收集脉冲神经网络相关的顶会顶刊以及CNS论文和代码，正在持续更新中。

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

Offical code of "QKFormer: Hierarchical Spiking Transformer using Q-K Attention" (NeurIPS 2024)

Spikingformer: A Key Foundation Model for Spiking Neural Networks (AAAI 2026)

The official PyTorch code for ICLR'22 Paper "Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System""

Official PyTorch implementation of paper "A Hybrid Compact Neural Architecture for Visual Place Recognition" by M. Chancán (RA-L & ICRA 2020) https://doi.org/10.1109/LRA.2020.2967324

Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation

Spiking Global-Local Fusion Transformer

Implementation for the paper "SpaceNet: Make Free Space For Continual Learning" in PyTorch.

Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal

Simple_Visual_Odometry_V1_01

Revolutionary database architecture combining neuromorphic computing, quantum-inspired algorithms, and DNA-storage principles for ultra-efficient edge computing applications based on SQL Queries

My first messy but working AI architecture. Have fun :-)

This project implements a neuromorphic NoC using RISC-V nodes with custom hardware to simulate spiking neural networks. Tested on an FPGA, it supports up to 1,024 neurons and uses dedicated FIFO buffers for stable, low-power operation. The design combines RISC-V flexibility with hardware acceleration for efficient small-scale SNN applications.

This project implements a neuromorphic Network-on-Chip using customized RV32IMF RISC-V cores, a 2D mesh NoC, and neuron bank hardware to accelerate spiking neural networks. It supports event-driven spike communication via custom ISA extensions and AXI interfaces, and is implemented and tested on an FPGA.

🤖 Build personalized AI systems that adapt to mood, energy, and creative flow, enhancing collaboration through emotional intelligence and dynamic interactions.

This project implements a neuromorphic architecture using parallel RISC-V processing elements to model spiking neurons, either individually or virtually grouped. It will also develop encoder and decoder modules to benchmark the system on classical machine learning tasks.

Biologically plausible cognitive architecture: structural memory, thought formation, and language without gradients or backpropagation