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- W2130589631 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." @default.
- W2130589631 created "2016-06-24" @default.
- W2130589631 creator A5005884869 @default.
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- W2130589631 date "2009-05-01" @default.
- W2130589631 modified "2023-10-13" @default.
- W2130589631 title "Competitive STDP-Based Spike Pattern Learning" @default.
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- W2130589631 doi "https://doi.org/10.1162/neco.2008.06-08-804" @default.
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