mlsanityData to 7 Oct 2026

Brain-inspired learning in artificial neural networks: a review

arXiv 2305.11252 ↗First posted 18 May 2023cs.NE cs.AI cs.LGcited by 26

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Abstract (arXiv)

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics. However, there exist fundamental differences between ANNs' operating mechanisms and those of the biological brain, particularly concerning learning processes. This paper presents a comprehensive review of current brain-inspired learning representations in artificial neural networks. We investigate the integration of more biologically plausible mechanisms, such as synaptic plasticity, to enhance these networks' capabilities. Moreover, we delve into the potential advantages and challenges accompanying this approach. Ultimately, we pinpoint promising avenues for future research in this rapidly advancing field, which could bring us closer to understanding the essence of intelligence.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 64 of its 164 references that are in the map. First posted on arXiv 18 May 2023.

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64 of 164 references.

References · 64

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Citing papers per year: 2023: 2; 2024: 9; 2025: 8; 2026: 7.

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