CerebelluMorphic: large-scale neuromorphic model and architecture for supervised motor learning

Yang, Shuangming, Wang, Jiang, Zhang, Nan, Deng, Bin, Pang, Yanwei, and Rahimi Azghadi, Mostafa (2022) CerebelluMorphic: large-scale neuromorphic model and architecture for supervised motor learning. IEEE Transactions on Neural Networks and Learning Systems. (In Press)

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The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.

Item ID: 68684
Item Type: Article (Research - C1)
ISSN: 2162-2388
Keywords: Brain modeling; Cerebellum; Neurons; Biological system modeling; Computational modeling; Neuromorphics; Computer architecture
Copyright Information: © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Funders: National Natural Science Foundation of China (NNSFC), Natural Science Foundation of Tianjin City (NSFoTC), China Postdoctoral Science Foundation (CPDSF)
Projects and Grants: NNSFC 10.13039/501100001809, NSFoTC 10.13039/501100006606, CPDSF 10.13039/501100002858
Date Deposited: 20 Jul 2021 03:50
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400903 Digital processor architectures @ 50%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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