ArticleFrontiers in computational neuroscience2014
Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity.
Article in Frontiers in computational neuroscience, 2014. 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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17 citing papers in PubMed.
- Data-driven mouse motor thalamus model reveals topography and spatial weight scaling govern spindle dynamics.Communications biology · 2026Article
- A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing.Nature communications · 2023Article
- New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics.Frontiers in computational neuroscience · 2023Article
- A model of head direction and landmark coding in complex environments.PLoS computational biology · 2021Article
- Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep.PLoS computational biology · 2021Article
- Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning.Frontiers in neuroscience · 2020Article
- Network structure and input integration in competing firing rate models for decision-making.Journal of computational neuroscience · 2019Article
- From statistical inference to a differential learning rule for stochastic neural networks.Interface focus · 2018Article
- Winner-take-all in a phase oscillator system with adaptation.Scientific reports · 2018Article
- A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences.Frontiers in neuroscience · 2018Article
- A Neurodynamic Model of Feature-Based Spatial Selection.Frontiers in psychology · 2018Article
- Article
- Thalamic Inhibition: Diverse Sources, Diverse Scales.Trends in neurosciences · 2016Review
- Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.PLoS computational biology · 2016Article
- Emergent Spatial Patterns of Excitatory and Inhibitory Synaptic Strengths Drive Somatotopic Representational Discontinuities and their Plasticity in a Computational Model of Primary Sensory Cortical Area 3b.Frontiers in computational neuroscience · 2016Article
- Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.Frontiers in computational neuroscience · 2015Article
- A framework for plasticity implementation on the SpiNNaker neural architecture.Frontiers in neuroscience · 2014Article
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4 authors.
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Abstract
Winner-Take-All (WTA) networks are recurrently connected populations of excitatory and inhibitory neurons that represent promising candidate microcircuits for implementing cortical computation. WTAs can perform powerful computations, ranging from signal-restoration to state-dependent processing. However, such networks require fine-tuned connectivity parameters to keep the network dynamics within stable operating regimes. In this article, we show how such stability can emerge autonomously through an interaction of biologically plausible plasticity mechanisms that operate simultaneously on all excitatory and inhibitory synapses of the network. A weight-dependent plasticity rule is derived from the triplet spike-timing dependent plasticity model, and its stabilization properties in the mean-field case are analyzed using contraction theory. Our main result provides simple constraints on the plasticity rule parameters, rather than on the weights themselves, which guarantee stable WTA behavior. The plastic network we present is able to adapt to changing input conditions, and to dynamically adjust its gain, therefore exhibiting self-stabilization mechanisms that are crucial for maintaining stable operation in large networks of interconnected subunits. We show how distributed neural assemblies can adjust their parameters for stable WTA function autonomously while respecting anatomical constraints on neural wiring.
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