Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article numbere0180174
Journal / PublicationPLoS ONE
Issue number8
Publication statusPublished - 10 Aug 2017
Externally publishedYes


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.

Research Area(s)

  • Action Potentials, Auditory Cortex, Neurons, Naural networks, Nerve fibers, Learning, Jitter, Neurophysiology