ArticleScientific reports2022
Efficient dendritic learning as an alternative to synaptic plasticity hypothesis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.Nature communications · 2025Article
- Article
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.ArXiv · 2024Article
- Article
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.American journal of neurodegenerative disease · 2024Article
- Enhancing the accuracies by performing pooling decisions adjacent to the output layer.Scientific reports · 2023Article
- Spontaneous activity in cortical neurons is stereotyped and non-Poisson.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Efficient shallow learning as an alternative to deep learning.Scientific reports · 2023Article
- Cellular Localization of Orexin 1 Receptor in Human Hypothalamus and Morphological Analysis of Neurons Expressing the Receptor.Biomolecules · 2023Article
- Learning on tree architectures outperforms a convolutional feedforward network.Scientific reports · 2023Article
- Cellular computation and cognition.Frontiers in computational neuroscience · 2023Article
- Introduction: What Are Dendritic Spines?Advances in neurobiology · 2023Article
- Review
- Changes in dendritic complexity and spine morphology following BCG immunization in APP/PS1 mice.Human vaccines & immunotherapeutics · 2022Article
- Chemogenetic Silencing of Differentiating Cortical Neurons Impairs Dendritic and Axonal Growth.Frontiers in cellular neuroscience · 2022Article
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5 authors.
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Abstract
Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local adaptation between two connecting neurons and forms the foundation of machine learning. The main complexity of synaptic plasticity is that synapses and dendrites connect neurons in series and existing experiments cannot pinpoint the significant imprinted adaptation location. We showed efficient backpropagation and Hebbian learning on dendritic trees, inspired by experimental-based evidence, for sub-dendritic adaptation and its nonlinear amplification. It has proven to achieve success rates approaching unity for handwritten digits recognition, indicating realization of deep learning even by a single dendrite or neuron. Additionally, dendritic amplification practically generates an exponential number of input crosses, higher-order interactions, with the number of inputs, which enhance success rates. However, direct implementation of a large number of the cross weights and their exhaustive manipulation independently is beyond existing and anticipated computational power. Hence, a new type of nonlinear adaptive dendritic hardware for imitating dendritic learning and estimating the computational capability of the brain must be built.
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