A One-Shot Learning, Online-Tuning, Closed-Loop Epilepsy Management SoC with 0.97μJ/Classification and 97.8% Vector-Based Sensitivity

Zhang, Miaolin; Park, Jeong Hoan; Tsai, Chne-Wuen; Ng, Kian Ann; Lin, Longyang; Dong, Youming; Li, Jiamin; Tang, Tao; Wu, Han; Wu, Liuhao; Yoo, Jerald

doi:10.23919/vlsicircuits52068.2021.9492429

Cited by 10 publications

(1 citation statement)

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“…As such, neuromorphic circuits are also natural candidates for interfacing, that is exchanging information between brains and artificial computing systems. Such interfaces can take the form of reading interfaces [10][11][12][13][14][15]-which record neural activity and/or decode their information content, for example for decoding motor signals and use them to drive prostheses -, writing interfaces [13,16,17]-which inject information into the nervous system by stimulating the tissue and evoking information-rich neural activity, for example circuits that take tactile or visual information with artificial sensors and then inject it into injured neural tissue to restore sensory function-, or closed-loop interfaces [13,[18][19][20][21] that include both reading and writing interfaces.…”

Section: Introductionmentioning

confidence: 99%

Constraints on the design of neuromorphic circuits set by the properties of neural population codes

Panzeri

Janotte

Pequeno-Zurro

et al. 2023

Neuromorph. Comput. Eng.

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In the brain, information is encoded, transmitted and used to inform behaviour at the level of timing of action potentials distributed over population of neurons. To implement neural-like systems in silico, to emulate neural function, and to interface successfully with the brain, neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain. To facilitate the cross-talk between neuromorphic engineering and neuroscience, in this Review we first critically examine and summarize emerging recent findings about how population of neurons encode and transmit information. We examine the effects on encoding and readout of information for different features of neural population activity, namely the sparseness of neural representations, the heterogeneity of neural properties, the correlations among neurons, and the time scales (from short to long) at which neurons encode information and maintain it consistently over time. Finally, we critically elaborate on how these facts constrain the design of information coding in neuromorphic circuits. We focus primarily on the implications for designing neuromorphic circuits that communicate with the brain, as in this case it is essential that artificial and biological neurons use compatible neural codes. However, we also discuss implications for the design of neuromorphic systems for implementation or emulation of neural computation.

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