The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Competitive STDP-Based Spike Pattern Learning
Abstract
Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.
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Cites
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Neural syntax: cell assemblies, synapsembles, and readers
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Polychronization: Computation with Spikes
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Cited by
Neural syntax: cell assemblies, synapsembles, and readers
Buzsáki, G. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.023 · Cited by 1,289
The Spike-Timing Dependence of Plasticity
Feldman, D. E. (2012) · Neuron · doi:10.1016/j.neuron.2012.08.001 · Cited by 1,071
Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., & Indiveri, G. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00141 · Cited by 698
Learning through ferroelectric domain dynamics in solid-state synapses
Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., Girod, S., Carrétéro, C., Garcia, K., Xavier, S., et al. (2017) · Nature Communications · doi:10.1038/ncomms14736 · Cited by 600
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
Zamarreño-Ramos, C., Camuñas-Mesa, L. A., Pérez-Carrasco, J. A., Masquelier, T., Serrano-Gotarredona, T., & Linares-Barranco, B. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00026 · Cited by 558
Self-citation
Metadata
- Authors
- Masquelier, T., Guyonneau, R., & Thorpe, S. J.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2009-05
- Volume
- 21
- Issue
- 5
- Pages
- 1259-1276
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2130589631
- Cited by
- 340
- References
- 58
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- closed
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