ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2013
Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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53 citing papers in PubMed.
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- Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex.PLoS computational biology · 2025Article
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- A multi-stage auditory model for binaural sound localization using the locally competitive algorithm.Scientific reports · 2025Article
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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
- Interneuronal modulations as a functional switch for cortical computations: mechanisms and implication for disease.Frontiers in cellular neuroscience · 2024Article
- Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo.Nature neuroscience · 2023Article
- The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.Nature neuroscience · 2023Article
- Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Computational methods to study information processing in neural circuits.Computational and structural biotechnology journal · 2023Review
- Enhanced representation learning with temporal coding in sparsely spiking neural networks.Frontiers in computational neuroscience · 2023Article
- Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices.PLoS computational biology · 2022Article
- Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity.PLoS computational biology · 2021Article
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3 authors.
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Abstract
Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.
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