Neuromorphic Computing

Tripti R, Kulkarni and Nandan, P and Monisha S., Monisha S. S (2025) Neuromorphic Computing. International Journal of Trend in Scientific Research and Development, 9 (3). pp. 97-103. ISSN 2456-6470

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Abstract

Neuromorphic computing refers to neural-inspired systems designed for non–von Neumann architectures that integrate principles from neuroscience, machine learning, AI, hardware design, and materials science. Initially focused on analog circuits mimicking biological neurons and synapses, the field has expanded to encompass a broad range of hardware and software systems. Core features of neuromorphic systems include co-located memory and computation, simple communication between neurons and synapses, and local learning capabilities. Many also exhibit spiking behavior, nonlinear dynamics, high connectivity, plasticity, robustness, and the ability to process noisy or incomplete data. These systems are typically event-driven, enabling low-power operation and emphasizing temporal dynamics. Their development requires interdisciplinary collaboration across neuroscience, computer science, engineering, and materials science.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	14 May 2025 12:31
Last Modified:	14 May 2025 12:31
URI:	http://eprints.umsida.ac.id/id/eprint/16068

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