| Autor: |
Guobin Shen, Dongcheng Zhao, Yi Zeng |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Patterns, Vol 3, Iss 6, Pp 100522- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2666-3899 |
| DOI: |
10.1016/j.patter.2022.100522 |
| Popis: |
Summary: The spiking neural network (SNN) mimics the information-processing operation in the human brain. Directly applying backpropagation to the training of the SNN still has a performance gap compared with traditional deep neural networks. To address the problem, we propose a biologically plausible spatial adjustment that rethinks the relationship between membrane potential and spikes and realizes a reasonable adjustment of gradients to different time steps. It precisely controls the backpropagation of the error along the spatial dimension. Secondly, we propose a biologically plausible temporal adjustment to make the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of traditional spiking neurons. We have verified our algorithm on several datasets, and the experimental results have shown that our algorithm greatly reduces network latency and energy consumption while also improving network performance. The bigger picture: The spiking neural network (SNN) captures more important aspects of brain information processing and has been applied to various domains. The biggest problem restricting the development of SNN is the training algorithm. Backpropagation (BP)-based training has extended SNNs to more complex network structures and datasets. However, the traditional design of BP ignores the dynamic characteristics of SNNs and is not biologically plausible. This paper rethinks the problems in BP-based SNNs and proposes a biologically plausible spatiotemporal adjustment to replace the traditional artificial design. The adjustment greatly improves the performance of the SNNs and reduces energy consumption and latency. The long-term ambition of this research is to take more inspiration on learning mechanisms and structures from the cognitive brain at different levels of details to build even more biologically plausible SNNs as a foundation for future artificial intelligence models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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