Yujie Wu

TL;DR: The Tianjic chip is presented, which integrates neuroscience-oriented and computer-science-oriented approaches to artificial general intelligence to provide a hybrid, synergistic platform and is expected to stimulate AGI development by paving the way to more generalized hardware platforms.

TL;DR: A spatio-temporal backpropagation (STBP) algorithm for training high-performance SNNs is proposed, which combines the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD), and does not require any additional complicated skill.

TL;DR: In this paper, the authors propose a neuron normalization technique to adjust the neural selectivity and develop a direct learning algorithm for deep SNNs via narrowing the rate coding window and converting the leaky integrate-and-fire (LIF) model into an explicitly iterative version.

TL;DR: This work proposes a neuron normalization technique to adjust the neural selectivity and develops a direct learning algorithm for deep SNNs and presents a Pytorch-based implementation method towards the training of large-scale Snns.

TL;DR: This paper designs a series of contrast tests using different types of datasets (ANN-oriented and SNN-oriented), diverse processing models, signal conversion methods, and learning algorithms, and recommends the most suitable model for each scenario and highlights the urgent need to build a benchmarking framework for SNNs with broader tasks, datasets, and metrics.