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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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Submitted on : Wednesday, May 13, 2009 - 12:01:49 PM
Last modification on : Friday, January 10, 2020 - 9:08:47 PM




Timothée Masquelier, Rudy Guyonneau, Simon J Thorpe. Competitive STDP-Based Spike Pattern Learning.. Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2008, 21 (5), pp.1259-1276. ⟨10.1162/neco.2008.06-08-804⟩. ⟨hal-00383703⟩



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