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
Spiking Neural Networks as a Controller for Emergent Swarm Agents
Kevin Zhu, Connor Mattson, Shay Snyder, Ricardo Vega, Daniel S. Brown, Maryam Parsa, Cameron Nowzari
Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model
Shibo Zhou, Bo Yang, Mengwen Yuan, Runhao Jiang, Rui Yan, Gang Pan, Huajin Tang
Twin Network Augmentation: A Novel Training Strategy for Improved Spiking Neural Networks and Efficient Weight Quantization
Lucas Deckers, Benjamin Vandersmissen, Ing Jyh Tsang, Werner Van Leekwijck, Steven Latré
Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks
Lingxin Jin, Meiyu Lin, Wei Jiang, Jinyu Zhan