ArticlePLoS computational biology2016
Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
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- Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Learning what matters: Synaptic plasticity with invariance to second-order input correlations.PLoS computational biology · 2024Article
- NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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- Efficient neural codes naturally emerge through gradient descent learning.Nature communications · 2022Article
- Weight dependence in BCM leads to adjustable synaptic competition.Journal of computational neuroscience · 2022Article
- Sub-Optimality of the Early Visual System Explained Through Biologically Plausible Plasticity.Frontiers in neuroscience · 2021Article
- An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features.Vision (Basel, Switzerland) · 2019Article
- When the goal is to generate a series of activities: A self-organized simulated robot arm.PloS one · 2019Article
- STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.PLoS computational biology · 2019Article
- Theoretical Models of Neural Development.iScience · 2018Review
- Article
- Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis.Scientific reports · 2018Article
- Hebbian plasticity requires compensatory processes on multiple timescales.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
- A Local Learning Rule for Independent Component Analysis.Scientific reports · 2016Article
- Toward an Integration of Deep Learning and Neuroscience.Frontiers in computational neuroscience · 2016Article
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2 authors.
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Abstract
The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.
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