Feb. 2, 2024, 3:42 p.m. | Zecheng Hao Xinyu Shi Zhiyu Pan Yujia Liu Zhaofei Yu Tiejun Huang

cs.CV updates on arXiv.org arxiv.org

Compared to traditional Artificial Neural Network (ANN), Spiking Neural Network (SNN) has garnered widespread academic interest for its intrinsic ability to transmit information in a more biological-inspired and energy-efficient manner. However, despite previous efforts to optimize the learning gradients and model structure of SNNs through various methods, SNNs still lag behind ANNs in terms of performance to some extent. The recently proposed multi-threshold model provides more possibilities for further enhancing the learning capability of SNNs. In this paper, we rigorously …

academic ann artificial cs.ai cs.cv cs.ne energy hierarchical information intrinsic network neural network performance snn spiking neural network threshold through

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York