Unsupervised Learning of Temporal Features for Word Categorization in a Spiking Neural Network Model of the Auditory Brain
Irina Higgins, Simon Stringer, Jan Schnupp
The paper develops a spiking neural network model to show that temporal encoding in polychronous groups better represents speaker-independent word identity than rate encoding.
bioRxiv · Jun 19, 2016Neuroscience