Sparsifying Spiking Networks through Local Rhythms

Autor:	Olin-Ammentorp, Wilkie
Rok vydání:	2023
Předmět:	Computer Science - Neural and Evolutionary Computing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, I demonstrate that spiking neural networks can prevent the transmission of spikes representing values close to zero using local information. This can reduce the amount of energy required for communication and computation in these networks while preserving accuracy. Additionally, this demonstrates a novel application of biologically observed spiking rhythms. Comment: 4 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.10191 Zobrazit plný text záznamu View this record from Arxiv