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

February 18, 2023

NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles
Craig Iaboni, Thomas Kelly, Pramod Abichandani
Deep Neural Network Data Detection Spiking Neural Network Localization Focus Neuromorphic Datasets Pedestrian Engagement Neuromorphic Sensor Nagoya University

February 15, 2023

Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch
Davide Liberato Manna, Alex Vicente-Sola, Paul Kirkland, Trevor Joseph Bihl, Gaetano Di Caterina
Neural Network New Framework Raw Data Spiking Neural Network Neuromorphic Computing Neuron Model Neuroscience Research

February 14, 2023

Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation
Ye Yue, Marc Baltes, Nidal Abujahar, Tao Sun, Charles D. Smith, Trevor Bihl, Jundong Liu
Neural Network Spiking Neural Network Hybrid Neural Network Hippocampus Segmentation Spike Based Backpropagation

February 13, 2023

February 8, 2023

The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks
Clemens JS Schaefer, Pooria Taheri, Mark Horeni, Siddharth Joshi
Deep Neural Network Spiking Neural Network State of the Art Quantization Operator Edge Pruning Balancing Weight Dynamic Vision Sensor Hardware Error

February 6, 2023

Dynamic Training of Liquid State Machines
Pavithra Koralalage, Ireoluwa Fakeye, Pedro Machado, Jason Smith, Isibor Kennedy Ihianle, Salisu Wada Yahaya, Andreas Oikonomou, Ahmad Lotfi
Neural Network Spiking Neural Network Spike Train Dynamic Training

February 4, 2023

Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu
Spiking Neural Network Neural Network Output Neuromorphic Chip SNN Conversion Membrane Potential

February 2, 2023

February 1, 2023

SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin
Spiking Neural Network Neuromorphic Hardware Spike Based SPIDER Max

January 29, 2023

Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns
Guobin Shen, Dongcheng Zhao, Yi Zeng
Spiking Neural Network Neuromorphic Hardware Spike Train Neuromorphic Datasets

January 23, 2023

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
Dengyu Wu, Gaojie Jin, Han Yu, Xinping Yi, Xiaowei Huang
Spiking Neural Network Regularization Model Spiking Neural Network Conversion Knowledge Cutoff

January 18, 2023

Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning
Yinqian Sun, Yi Zeng, Feifei Zhao, Zhuoya Zhao
Spiking Neural Network Distributional Policy Gradient Compartmental Neuron Population Encoding

January 12, 2023

Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimization
Ayesha Siddique, Khaza Anuarul Hoque
Spiking Neural Network Neuromorphic Hardware Neuromorphic Datasets DNN Accelerator

December 25, 2022

Sitting Posture Recognition Using a Spiking Neural Network
Jianquan Wang, Basim Hafidh, Haiwei Dong, Abdulmotaleb El Saddik
Spiking Neural Network Single Neuron Level Sitting POSTURE Posture Measurement

December 24, 2022

Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks
Rachmad Vidya Wicaksana Putra, Muhammad Shafique
Spiking Neural Network SNN Model Plausible Learning Biological Learning

December 23, 2022

December 20, 2022

Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Gourav Datta, Zeyu Liu, Peter A. Beerel
Neural Network Spiking Neural Network Low Latency Single Neural Network Adder Neural Network Low Power Computer Vision

Spiking Neural Network

Papers

NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles

Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch

Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation

Workload-Balanced Pruning for Sparse Spiking Neural Networks

Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data

The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks

Dynamic Training of Liquid State Machines

Reducing ANN-SNN Conversion Error through Residual Membrane Potential

Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition

OpenSpike: An OpenRAM SNN Accelerator

SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks

Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns

Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff

Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning

Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimization

Sitting Posture Recognition Using a Spiking Neural Network

Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks

An Exact Mapping From ReLU Networks to Spiking Neural Networks

hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2

Hoyer regularizer is all you need for ultra low-latency spiking neural networks