An Ultralow-Power CMOS Integrated and Fire Neuron for Neuromorphic Computing

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dc.contributor.authorHaque, M.N.
dc.contributor.authorKhan, S.R.
dc.contributor.authorIslam, M.T.
dc.contributor.authorNaik, J.D.
dc.contributor.authorAl-Shidaifat, A.D.
dc.contributor.authorKumar, S.
dc.contributor.authorSong, H.
dc.date.accessioned2026-02-06T06:35:06Z
dc.date.issued2023
dc.description.abstractVery large-scale integration (VLSI) implementations of spiking neurons are vital for a range of applications, from high-speed modeling of large neural systems to real-time behavioral systems and bidirectional brain-machine interfaces. The circuit solution utilized to implement the silicon neuron is determined by the application’s needs. This paper describes an ultralow-power analog circuit for realizing a leaky integrate and fire neuron model. The suggested circuit comprises parts for executing spike-frequency adaptation and modifying the neuron’s threshold voltage, in addition to being designed for low-power consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.identifier.citationLecture Notes in Networks and Systems, 2023, Vol.554, , p. 457-463
dc.identifier.issn23673370
dc.identifier.urihttps://doi.org/10.1007/978-981-19-6661-3_41
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29646
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectArtificial neuron
dc.subjectIntegrated and fire neuron
dc.subjectNeural network
dc.subjectNeuromorphic computing
dc.subjectSilicon neuron circuit
dc.titleAn Ultralow-Power CMOS Integrated and Fire Neuron for Neuromorphic Computing

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