title: 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.
authors.affiliation: Centre de Recherche Cerveau et Cognition, Université Toulouse 3, Centre National de la Recherche Scientifique, Faculté de Médecine de Rangueil, Toulouse 31062, France. timothee.masquelier@alum.mit.edu
authors.forename: Timothée
authors.initials: T
authors.lastname: Masquelier
authors.forename: Rudy
authors.initials: R
authors.lastname: Guyonneau
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Thorpe
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013997:Time Factors