Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Al-Shidaifat, A.D.Subject: search.filters.subject.Artificial neuron

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    An Ultralow-Power CMOS Integrated and Fire Neuron for Neuromorphic Computing
    (Springer Science and Business Media Deutschland GmbH, 2023) Haque, M.N.; Khan, S.R.; Islam, M.T.; Naik, J.D.; Al-Shidaifat, A.D.; Kumar, S.; Song, H.
    Very 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.