ArticleFrontiers in computational neuroscience2012
Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity.
Article in Frontiers in computational neuroscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules.eLife · 2024Article
- Article
- From synapse to network: models of information storage and retrieval in neural circuits.Current opinion in neurobiology · 2021Review
- Learning spatiotemporal signals using a recurrent spiking network that discretizes time.PLoS computational biology · 2020Article
- Unsupervised Learning of Persistent and Sequential Activity.Frontiers in computational neuroscience · 2019Article
- Memory replay in balanced recurrent networks.PLoS computational biology · 2017Article
- Oscillation-induced signal transmission and gating in neural circuits.PLoS computational biology · 2014Article
- Robust development of synfire chains from multiple plasticity mechanisms.Frontiers in computational neuroscience · 2014Article
- Article
- Pattern association and consolidation emerges from connectivity properties between cortex and hippocampus.PloS one · 2014Article
- A reafferent and feed-forward model of song syntax generation in the Bengalese finch.Journal of computational neuroscience · 2011Article
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4 authors.
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Abstract
Synfire chains have long been proposed to generate precisely timed sequences of neural activity. Such activity has been linked to numerous neural functions including sensory encoding, cognitive and motor responses. In particular, it has been argued that synfire chains underlie the precise spatiotemporal firing patterns that control song production in a variety of songbirds. Previous studies have suggested that the development of synfire chains requires either initial sparse connectivity or strong topological constraints, in addition to any synaptic learning rules. Here, we show that this necessity can be removed by using a previously reported but hitherto unconsidered spike-timing-dependent plasticity (STDP) rule and activity-dependent excitability. Under this rule the network develops stable synfire chains that possess a non-trivial, scalable multi-layer structure, in which relative layer sizes appear to follow a universal function. Using computational modeling and a coarse grained random walk model, we demonstrate the role of the STDP rule in growing, molding and stabilizing the chain, and link model parameters to the resulting structure.
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