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

@inproceedings{Higgins2016UnsupervisedLO,
  title={Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain},
  author={Irina Higgins and Simon Maitland Stringer and Jan W. H. Schnupp},
  booktitle={PloS one},
  year={2016}
}
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… CONTINUE READING
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