mlsanityData to 14 Oct 2026

A Review of Neuroscience-Inspired Machine Learning

arXiv 2403.18929 ↗First posted 16 Feb 2024cs.NE cs.LGcited by 6

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Abstract (arXiv)

One major criticism of deep learning centers around the biological implausibility of the credit assignment schema used for learning -- backpropagation of errors. This implausibility translates into practical limitations, spanning scientific fields, including incompatibility with hardware and non-differentiable implementations, thus leading to expensive energy requirements. In contrast, biologically plausible credit assignment is compatible with practically any learning condition and is energy-efficient. As a result, it accommodates hardware and scientific modeling, e.g. learning with physical systems and non-differentiable behavior. Furthermore, it can lead to the development of real-time, adaptive neuromorphic processing systems. In addressing this problem, an interdisciplinary branch of artificial intelligence research that lies at the intersection of neuroscience, cognitive science, and machine learning has emerged. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks, discussing the solutions they provide for different scientific fields as well as their advantages on CPUs, GPUs, and novel implementations of neuromorphic hardware. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 47 of its 81 references that are in the map. First posted on arXiv 16 Feb 2024.

Builds on

47 of 81 references.

References · 47

  1. 2022The Forward-Forward Algorithm: Some Preliminary Investigationscited by 266
  2. 2016Direct Feedback Alignment Provides Learning in Deep Neural Networkscited by 240
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35 more
  1. 2020Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architecturescited by 56
  2. 2021Brain-inspired computing: We need a master plancited by 51
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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024Self-Contrastive Forward-Forward Algorithmcited by 17
  2. 2024Tight Stability, Convergence, and Robustness Bounds for Predictive Coding Networkscited by 3
  3. 2024Machine Unlearning using Forgetting Neural Networkscited by 2
  4. 2025CosmoCore Affective Dream-Replay Reinforcement Learning for Code Generationcited by 1
  5. 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systemscited by 0
  6. 2025Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learningcited by 0