mlsanityData to 7 Oct 2026

Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks

arXiv 2106.07490 ↗First posted 14 Jun 2021q-bio.NCcited by 15

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Abstract (arXiv)

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jun 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 4; 2025: 2; 2026: 1.

15 papers.

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  1. 2023Simplicial Hopfield networkscited by 16
  2. 2023Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neuronscited by 12
  3. 2021A Normative and Biologically Plausible Algorithm for Independent Component Analysiscited by 8
  4. 2021Dendritic Self-Organizing Maps for Continual Learningcited by 5
  5. 2023The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Taskscited by 5
  6. 2024Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithmcited by 3
  7. 2024Associative memory inspires improvements for in-context learning using a novel attention residual stream architecturecited by 3
  8. 2024Two Tales of Single-Phase Contrastive Hebbian Learningcited by 2
  9. 2024Augmenting learning in neuro-embodied systems through neurobiological first principlescited by 2
  10. 2025Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamicscited by 2
  11. 2025Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networkscited by 1
  12. 2022Parameter efficient dendritic-tree neurons outperform perceptronscited by 0
3 more
  1. 2022The Role Of Biology In Deep Learningcited by 0
  2. 2023Dendritic Integration Based Quadratic Neural Networks Outperform Traditional Aritificial Onescited by 0
  3. 2026Perforated Neural Networks for Keyword Spottingcited by 0