Cargando…

MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks

Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based...

Descripción completa

Detalles Bibliográficos
Autores principales: Yu, Chengting, Du, Yangkai, Chen, Mufeng, Wang, Aili, Wang, Gaoang, Li, Erping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531034/
https://www.ncbi.nlm.nih.gov/pubmed/36203801
http://dx.doi.org/10.3389/fnins.2022.945037
_version_ 1784801817071190016
author Yu, Chengting
Du, Yangkai
Chen, Mufeng
Wang, Aili
Wang, Gaoang
Li, Erping
author_facet Yu, Chengting
Du, Yangkai
Chen, Mufeng
Wang, Aili
Wang, Gaoang
Li, Erping
author_sort Yu, Chengting
collection PubMed
description Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based algorithms partially ignore bio-interpretability. In modeling spike activity for biological plausible BP-based SNNs, we examine three properties: multiplicity, adaptability, and plasticity (MAP). Regarding multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple-spike transmission to improve model robustness in discrete time iterations. To realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to reduce spike activities for enhanced efficiency. For plasticity, we propose a trainable state-free synapse that models spike response current to increase the diversity of spiking neurons for temporal feature extraction. The proposed SNN model achieves competitive performances on the N-MNIST and SHD neuromorphic datasets. In addition, experimental results demonstrate that the proposed three aspects are significant to iterative robustness, spike efficiency, and the capacity to extract spikes' temporal features. In summary, this study presents a realistic approach for bio-inspired spike activity with MAP, presenting a novel neuromorphic perspective for incorporating biological properties into spiking neural networks.
format Online
Article
Text
id pubmed-9531034
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95310342022-10-05 MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks Yu, Chengting Du, Yangkai Chen, Mufeng Wang, Aili Wang, Gaoang Li, Erping Front Neurosci Neuroscience Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based algorithms partially ignore bio-interpretability. In modeling spike activity for biological plausible BP-based SNNs, we examine three properties: multiplicity, adaptability, and plasticity (MAP). Regarding multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple-spike transmission to improve model robustness in discrete time iterations. To realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to reduce spike activities for enhanced efficiency. For plasticity, we propose a trainable state-free synapse that models spike response current to increase the diversity of spiking neurons for temporal feature extraction. The proposed SNN model achieves competitive performances on the N-MNIST and SHD neuromorphic datasets. In addition, experimental results demonstrate that the proposed three aspects are significant to iterative robustness, spike efficiency, and the capacity to extract spikes' temporal features. In summary, this study presents a realistic approach for bio-inspired spike activity with MAP, presenting a novel neuromorphic perspective for incorporating biological properties into spiking neural networks. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9531034/ /pubmed/36203801 http://dx.doi.org/10.3389/fnins.2022.945037 Text en Copyright © 2022 Yu, Du, Chen, Wang, Wang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Yu, Chengting
Du, Yangkai
Chen, Mufeng
Wang, Aili
Wang, Gaoang
Li, Erping
MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
title MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
title_full MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
title_fullStr MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
title_full_unstemmed MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
title_short MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
title_sort map-snn: mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531034/
https://www.ncbi.nlm.nih.gov/pubmed/36203801
http://dx.doi.org/10.3389/fnins.2022.945037
work_keys_str_mv AT yuchengting mapsnnmappingspikeactivitieswithmultiplicityadaptabilityandplasticityintobioplausiblespikingneuralnetworks
AT duyangkai mapsnnmappingspikeactivitieswithmultiplicityadaptabilityandplasticityintobioplausiblespikingneuralnetworks
AT chenmufeng mapsnnmappingspikeactivitieswithmultiplicityadaptabilityandplasticityintobioplausiblespikingneuralnetworks
AT wangaili mapsnnmappingspikeactivitieswithmultiplicityadaptabilityandplasticityintobioplausiblespikingneuralnetworks
AT wanggaoang mapsnnmappingspikeactivitieswithmultiplicityadaptabilityandplasticityintobioplausiblespikingneuralnetworks
AT lierping mapsnnmappingspikeactivitieswithmultiplicityadaptabilityandplasticityintobioplausiblespikingneuralnetworks