ArticleNature communications2025
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Learning reorganizes dendritic and stabilizes axon initial segment inhibitory synapses in CA1 pyramidal neurons.Nature communications · 2026Article
- Cerebellum-inspired neural network of supervised learning with tensor-based sparse coding for multi-class classification.PLoS computational biology · 2026Article
- A computational theory of short-term synaptic plasticity: synapses learn to tell time.Research square · 2026Article
- Dendritic nonlinearities mitigate communication costs.Patterns (New York, N.Y.) · 2026Article
- Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing.Nano-micro letters · 2026Review
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Review
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- AI-Empowered Electrochemical Sensors for Biomedical Applications: Technological Advances and Future Challenges.Biosensors · 2025Review
- A Bio-Inspired Learning Dendritic Motion Detection Framework with Direction-Selective Horizontal Cells.Biomimetics (Basel, Switzerland) · 2025Article
- Article
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.Nature communications · 2025Article
- Biophysical and computational insights from modeling human cortical pyramidal neurons.Frontiers in neuroscience · 2025Review
- Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.Research (Washington, D.C.) · 2025Review
- Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.American journal of neurodegenerative disease · 2024Article
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2 authors.
Funding
Abstract
Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, making them energy-intensive and prone to overfitting. Here, we show that a new ANN architecture that incorporates the structured connectivity and restricted sampling properties of biological dendrites counteracts these limitations. We find that dendritic ANNs are more robust to overfitting and match or outperform traditional ANNs on several image classification tasks while using significantly fewer trainable parameters. These advantages are likely the result of a different learning strategy, whereby most of the nodes in dendritic ANNs respond to multiple classes, unlike classical ANNs that strive for class-specificity. Our findings suggest that the incorporation of dendritic properties can make learning in ANNs more precise, resilient, and parameter-efficient and shed new light on how biological features can impact the learning strategies of ANNs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.