ArticlePLoS computational biology2021
Dendritic normalisation improves learning in sparsely connected artificial neural networks.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.Nature communications · 2025Article
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.ArXiv · 2024Article
- A GPU-based computational framework that bridges neuron simulation and artificial intelligence.Nature communications · 2023Article
- Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network.Frontiers in computational neuroscience · 2023Review
- Introducing principles of synaptic integration in the optimization of deep neural networks.Nature communications · 2022Article
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3 authors.
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Abstract
Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial neural networks, which have the potential to be more computationally efficient than their fully-connected counterparts and more closely resemble the architectures of biological systems. We here present a normalisation, based on the biophysical behaviour of neuronal dendrites receiving distributed synaptic inputs, that divides the weight of an artificial neuron's afferent contacts by their number. We apply this dendritic normalisation to various sparsely-connected feedforward network architectures, as well as simple recurrent and self-organised networks with spatially extended units. The learning performance is significantly increased, providing an improvement over other widely-used normalisations in sparse networks. The results are two-fold, being both a practical advance in machine learning and an insight into how the structure of neuronal dendritic arbours may contribute to computation.
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