Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.
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ABSTRACT:
SUBMITTER: Higgins I
PROVIDER: S-EPMC5552261 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.
Higgins Irina I Stringer Simon S Schnupp Jan J
PloS one 20170810 8
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 ...[more]
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