PLOS_ONE_data.zip

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.