Yang, Shuangming, Wang, Haowen, Pang, Yanwei, Rahimi Azghadi, Mostafa, and Linares-Barranco, Bernabe (2024) NADOL: Neuromorphic Architecture for Spike-driven Online Learning By Dendrites. IEEE Transactions on Biomedical Circuits and Systems, 18 (1). pp. 186-199.

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Abstract

Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.

Item ID:	81674
Item Type:	Article (Research - C1)
ISSN:	1940-9990
Copyright Information:	© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	24 Jan 2024 01:04
FoR Codes:	40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400902 Digital electronic devices @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation @ 60%
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