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• W2950493407 endingPage "e0180174" @default.
• W2950493407 startingPage "e0180174" @default.
• W2950493407 abstract "The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable." @default.
• W2950493407 created "2019-06-27" @default.
• W2950493407 creator A5000981500 @default.
• W2950493407 creator A5010600835 @default.
• W2950493407 creator A5018830387 @default.
• W2950493407 date "2017-08-10" @default.
• W2950493407 modified "2023-09-24" @default.
• W2950493407 title "Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain" @default.
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• W2950493407 doi "https://doi.org/10.1371/journal.pone.0180174" @default.
• W2950493407 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5552261" @default.
• W2950493407 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28797034" @default.
• W2950493407 hasPublicationYear "2017" @default.
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