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
Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks
Yuhao Pan, Xiucheng Wang, Nan Cheng, Qi Qiu
Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting
Shuai Wang, Dehao Zhang, Kexin Shi, Yuchen Wang, Wenjie Wei, Jibin Wu, Malu Zhang
CADE: Cosine Annealing Differential Evolution for Spiking Neural Network
Runhua Jiang, Guodong Du, Shuyang Yu, Yifei Guo, Sim Kuan Goh, Ho-Kin Tang
Context Gating in Spiking Neural Networks: Achieving Lifelong Learning through Integration of Local and Global Plasticity
Jiangrong Shen, Wenyao Ni, Qi Xu, Gang Pan, Huajin Tang
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors
Mustafa Sakhai, Szymon Mazurek, Jakub Caputa, Jan K. Argasiński, Maciej Wielgosz
Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks
Lihao Wang, Zhaofei Yu