ArticleJournal of computational neuroscience2013
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks.
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 6 papers.
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Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- Article
- Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.PLoS computational biology · 2016Article
- A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Article
- Oscillatory neurocomputing with ring attractors: a network architecture for mapping locations in space onto patterns of neural synchrony.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2014Article
- Alternation of up and down states at a dynamical phase-transition of a neural network with spatiotemporal attractors.Frontiers in systems neuroscience · 2014Article
- Neural avalanches at the critical point between replay and non-replay of spatiotemporal patterns.PloS one · 2013Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at different time scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stable precise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.