Brain-Inspired Architecture for Spiking Neural Networks
Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes t...
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MDPI AG
2024-10-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/10/646 |
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| author | Fengzhen Tang Junhuai Zhang Chi Zhang Lianqing Liu |
| author_facet | Fengzhen Tang Junhuai Zhang Chi Zhang Lianqing Liu |
| author_sort | Fengzhen Tang |
| collection | DOAJ |
| description | Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture. |
| format | Article |
| id | doaj-art-865d8769ab3041eeaaaeea2116d0eb13 |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-865d8769ab3041eeaaaeea2116d0eb132025-08-20T02:11:14ZengMDPI AGBiomimetics2313-76732024-10-0191064610.3390/biomimetics9100646Brain-Inspired Architecture for Spiking Neural NetworksFengzhen Tang0Junhuai Zhang1Chi Zhang2Lianqing Liu3State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Nanta Street 114, Shenyang 110016, ChinaSpiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture.https://www.mdpi.com/2313-7673/9/10/646spiking neural networksself-adaptive codingsurrogate gradient backpropagationleaky integrate-and-fire neuron model |
| spellingShingle | Fengzhen Tang Junhuai Zhang Chi Zhang Lianqing Liu Brain-Inspired Architecture for Spiking Neural Networks Biomimetics spiking neural networks self-adaptive coding surrogate gradient backpropagation leaky integrate-and-fire neuron model |
| title | Brain-Inspired Architecture for Spiking Neural Networks |
| title_full | Brain-Inspired Architecture for Spiking Neural Networks |
| title_fullStr | Brain-Inspired Architecture for Spiking Neural Networks |
| title_full_unstemmed | Brain-Inspired Architecture for Spiking Neural Networks |
| title_short | Brain-Inspired Architecture for Spiking Neural Networks |
| title_sort | brain inspired architecture for spiking neural networks |
| topic | spiking neural networks self-adaptive coding surrogate gradient backpropagation leaky integrate-and-fire neuron model |
| url | https://www.mdpi.com/2313-7673/9/10/646 |
| work_keys_str_mv | AT fengzhentang braininspiredarchitectureforspikingneuralnetworks AT junhuaizhang braininspiredarchitectureforspikingneuralnetworks AT chizhang braininspiredarchitectureforspikingneuralnetworks AT lianqingliu braininspiredarchitectureforspikingneuralnetworks |