ArticleJournal of computational neuroscience2013
High-capacity embedding of synfire chains in a cortical network model.
Article in Journal of computational neuroscience, 2013. 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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Who cites it
9 citing papers in PubMed.
- Adaptive control of synaptic plasticity integrates micro- and macroscopic network function.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Review
- Article
- Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation.PLoS computational biology · 2018Article
- Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory.Annual review of neuroscience · 2017Review
- Memory replay in balanced recurrent networks.PLoS computational biology · 2017Article
- Dynamic effective connectivity in cortically embedded systems of recurrently coupled synfire chains.Journal of computational neuroscience · 2016Article
- Implications of polychronous neuronal groups for the continuity of mind.Cognitive processing · 2015Article
- Synconset waves and chains: spiking onsets in synchronous populations predict and are predicted by network structure.PloS one · 2013Article
- Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity.Frontiers in computational neuroscience · 2012Article
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Authors and funding
3 authors.
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Abstract
Synfire chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or synfire waves. An important question remains, how synfire chains can efficiently be embedded in cortical architecture. We present a model of synfire chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.
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