Spiking Neural Network

Spiking neural networks (SNNs), inspired by the brain's event-driven communication, aim to create energy-efficient artificial intelligence by processing information through binary spikes rather than continuous values. Current research emphasizes improving training efficiency through novel neuron models (e.g., parallel resonate and fire neurons, multi-compartment neurons), developing specialized weight initialization methods, and exploring various coding schemes (e.g., Poisson coding, stepwise weighted spike coding) to optimize performance and reduce energy consumption. This field is significant due to SNNs' potential for low-power applications in embedded systems, neuromorphic computing, and real-time signal processing tasks like robotic manipulation and brain-computer interfaces.

Papers

November 18, 2023

Pursing the Sparse Limitation of Spiking Deep Learning Structures
Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Le Yang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu
Spiking Neural Network Lottery Ticket Hypothesis Sparse Structure Convolutional Spiking Neural Network DNN Pruning

November 17, 2023

Is Conventional SNN Really Efficient? A Perspective from Network Quantization
Guobin Shen, Dongcheng Zhao, Tenglong Li, Jindong Li, Yi Zeng
High Efficiency Spiking Neural Network Visual Perspective Neuromorphic Datasets Synaptic Feedback Energy Efficient Neural Network Network Quantization

November 15, 2023

November 13, 2023

Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities
Huan Wang, Yan-Fu Li, Konstantinos Gryllias
Timely Survey Technical Challenge Spiking Neural Network Emerging Opportunity Fault Diagnosis Neuronal Dynamic Bio Inspired Neural Network

November 11, 2023

OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks
Yimeng Shan, Xuerui Qiu, Rui-jie Zhu, Ruike Li, Meng Wang, Haicheng Qu
Spiking Neural Network Residual Connection Spike Based Residual Learning

November 10, 2023

Programmable Superconducting Optoelectronic Single-Photon Synapses with Integrated Multi-State Memory
Bryce A. Primavera, Saeed Khan, Richard P. Mirin, Sae Woo Nam, Jeffrey M. Shainline
Spiking Neural Network Neuromorphic Computing Neuromorphic Hardware Memory System Optoelectronic Loop Neuron

November 9, 2023

November 5, 2023

Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks
Shuo Chen, Boxiao Liu, Zeshi Liu, Haihang You
Spiking Neural Network Network Pruning Pruning Framework Critical Neuron

October 25, 2023

Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking Neural Network Conformal Prediction Diverse Ensemble Recurrent Network Ensemble Model Latency Accuracy

October 23, 2023

Tensor Decomposition Based Attention Module for Spiking Neural Networks
Haoyu Deng, Ruijie Zhu, Xuerui Qiu, Yule Duan, Malu Zhang, Liangjian Deng
Neural Network Spiking Neural Network Tensor Decomposition Spiked Tensor Tensor Attention

October 11, 2023

October 10, 2023

Spiking PointNet: Spiking Neural Networks for Point Clouds
Dayong Ren, Zhe Ma, Yuanpei Chen, Weihang Peng, Xiaode Liu, Yuhan Zhang, Yufei Guo
Neural Network Point Cloud Spiking Neural Network Efficient Deep PointNet Model 3D Object Recognition Spiking Element Wise

October 9, 2023

October 8, 2023

Fully Spiking Neural Network for Legged Robots
Xiaoyang Jiang, Qiang Zhang, Jingkai Sun, Jiahang Cao, Jingtong Ma, Renjing Xu
Spiking Neural Network Legged Robot Humanoid Robot Quadruped Robot

October 5, 2023

Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks
Reza Ahmadvand, Sarah Safura Sharif, Yaser Mike Banad
External Control Spiking Neural Network Dynamical System Neuromorphic Computing Robotic System Linear Quadratic Regulator Neuromorphic Event Online Estimation

Spiking Neural Network

Papers

Pursing the Sparse Limitation of Spiking Deep Learning Structures

Is Conventional SNN Really Efficient? A Perspective from Network Quantization

Spiking NeRF: Representing the Real-World Geometry by a Discontinuous Representation

SparseSpikformer: A Co-Design Framework for Token and Weight Pruning in Spiking Transformer

Adversarially Robust Spiking Neural Networks Through Conversion

Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities

OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks

Programmable Superconducting Optoelectronic Single-Photon Synapses with Integrated Multi-State Memory

From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence

Rethinking Residual Connection in Training Large-Scale Spiking Neural Networks

Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks

Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks

Tensor Decomposition Based Attention Module for Spiking Neural Networks

An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks

SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition

Spiking PointNet: Spiking Neural Networks for Point Clouds

Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memory

Investigating Continuous Learning in Spiking Neural Networks

Fully Spiking Neural Network for Legged Robots

Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks