ArticleFrontiers in computational neuroscience2017
Mechanisms of Winner-Take-All and Group Selection in Neuronal Spiking Networks.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Micro-Stimulation Timing Framed Around an Averaged Theta Period of Stimulation Determines Hippocampal Recruitment in Cued Fear Conditioning.Hippocampus · 2026Article
- Selective inhibition in CA3: A mechanism for stable pattern completion through heterosynaptic plasticity.PLoS computational biology · 2025Article
- A tonically active master neuron modulates mutually exclusive motor states at two timescales.Science advances · 2024Article
- Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach.Frontiers in computational neuroscience · 2024Article
- Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware.Frontiers in neuroscience · 2022Article
- A model of head direction and landmark coding in complex environments.PLoS computational biology · 2021Article
- Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons.Journal of computational neuroscience · 2020Article
- Recurrent cortical circuits implement concentration-invariant odor coding.Science (New York, N.Y.) · 2018Article
- Winner-take-all in a phase oscillator system with adaptation.Scientific reports · 2018Article
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Abstract
A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not completely understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.
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