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

April 10, 2023

Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Network State of the Art Recurrent Layer Online Time Series Dependent Plasticity

April 8, 2023

RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
Spiking Neural Network Post Fault Trajectory Dead Neuron

April 2, 2023

SEENN: Towards Temporal Spiking Early-Exit Neural Networks
Yuhang Li, Tamar Geller, Youngeun Kim, Priyadarshini Panda
Spiking Neural Network Binary Spike SNN Conversion Early Exit Neural Network

March 31, 2023

Equivalence of Additive and Multiplicative Coupling in Spiking Neural Networks
Georg Börner, Fabio Schittler Neves, Marc Timme
Spiking Neural Network Collective Motion Optimal Coupling Feed Additive Emergent Collective Intrinsic Neural Field

March 24, 2023

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
Asude Aydin, Mathias Gehrig, Daniel Gehrig, Davide Scaramuzza
Spiking Neural Network Low Power Classical Neural Network Bio Inspired Neural Network Perception Latency

March 23, 2023

March 22, 2023

From Clean Room to Machine Room: Commissioning of the First-Generation BrainScaleS Wafer-Scale Neuromorphic System
Hartmut Schmidt, José Montes, Andreas Grübl, Maurice Güttler, Dan Husmann, Joscha Ilmberger, Jakob Kaiser, Christian Mauch, Eric Müller, Lars Sterzenbach, Johannes Schemmel, Sebastian Schmitt
Spiking Neural Network ROOM Layout BrainScaleS 2 Machine Classification Wafer Scale

March 20, 2023

MT-SNN: Enhance Spiking Neural Network with Multiple Thresholds
Xiaoting Wang, Yanxiang Zhang
Neural Network Spiking Neural Network Traditional Neural Network Leaky Integrate and Fire Multi Threshold

March 19, 2023

A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices
Kai Malcolm, Josue Casco-Rodriguez
Neural Network High Efficiency Optimization Purpose Spiking Neural Network Comprehensive Review Best Practice Biological Neural Network Neural Computation Neural Network Performance

March 17, 2023

Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
Chen Li, Edward Jones, Steve Furber
Spiking Neural Network Full Potential Low Latency SNN Model Neural Network Output Confidence Difference

March 14, 2023

March 8, 2023

Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
Tong Bu, Wei Fang, Jianhao Ding, PengLin Dai, Zhaofei Yu, Tiejun Huang
Neural Network Spiking Neural Network Low Latency High Accuracy SNN Conversion ANN SNN Conversion

February 28, 2023

Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo
Spiking Neural Network Back Propagation Neuromorphic Computing Memory Trace Neuromorphic Datasets Efficient Deep Spatial Learning

February 27, 2023

February 22, 2023

Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
Biswadeep Chakraborty, Saibal Mukhopadhyay
Spiking Neural Network Theoretical Understanding Design Principle Spike Timing Dependent Plasticity Neuronal Heterogeneity Synaptic Dynamic Spike Depth Estimation

February 21, 2023

Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu
Spiking Neural Network Neuromorphic Chip Ann Bp SNN Conversion

Spiking Neural Network

Papers

Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction

RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults

SEENN: Towards Temporal Spiking Early-Exit Neural Networks

Equivalence of Additive and Multiplicative Coupling in Spiking Neural Networks

A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training

MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks

From Clean Room to Machine Room: Commissioning of the First-Generation BrainScaleS Wafer-Scale Neuromorphic System

MT-SNN: Enhance Spiking Neural Network with Multiple Thresholds

A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices

Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence

Emergent Bio-Functional Similarities in a Cortical-Spike-Train-Decoding Spiking Neural Network Facilitate Predictions of Neural Computation

Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors

Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretching

Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks

Communication Trade-offs in Federated Learning of Spiking Neural Networks

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles

Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes