Synergies and Challenges in Biocomputers and Neuromorphic Computing: A Comprehensive Review

Abstract: The convergence of biocomputing and neuromorphic computing represents a pivotal shift in computing paradigms, aiming to emulate natural biological systems. This survey explores their core principles, prospects, hurdles, and current status, highlighting potential synergy. Biocomputing utilizes biological processes and materials for computations, inspired by living organisms' efficiency. Neuromorphic computing replicates brain neural networks for cognitive capabilities and energy efficiency. Despite promising progress with memristor crossbar networks, challenges like limited precision persist. Advancements in neuromorphic engineering enable interfacing silicon neurons with biological networks, exploring previously inaccessible neural properties. Neuromorphic VLSI systems offer compactness and low power consumption, facilitating potential clinical applications in neuro-engineering.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-02
DOI
https://doi.org/10.5281/zenodo.15789642
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Synergies and Challenges in Biocomputers and Neuromorphic Computing: A Comprehensive Review

Zenodo (CERN European Organization for Nuclear Research)
Advanced Memory and Neural Computing
article

Synergies and Challenges in Biocomputers and Neuromorphic Computing: A Comprehensive Review

article en

Abstract

Abstract: The convergence of biocomputing and neuromorphic computing represents a pivotal shift in computing paradigms, aiming to emulate natural biological systems. This survey explores their core principles, prospects, hurdles, and current status, highlighting potential synergy. Biocomputing utilizes biological processes and materials for computations, inspired by living organisms' efficiency. Neuromorphic computing replicates brain neural networks for cognitive capabilities and energy efficiency. Despite promising progress with memristor crossbar networks, challenges like limited precision persist. Advancements in neuromorphic engineering enable interfacing silicon neurons with biological networks, exploring previously inaccessible neural properties. Neuromorphic VLSI systems offer compactness and low power consumption, facilitating potential clinical applications in neuro-engineering.

Zenodo (CERN European Organization for Nuclear Research)
Marymount University (US)
Affordable and clean energy
Openalex Percentile: Top 57%
Advanced Memory and Neural Computing
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Synergies and Challenges in Biocomputers and Neuromorphic Computing: A Comprehensive Review · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS