Unsupervised Learning of Temporal Features for Word Categorization in a Spiking Neural Network Model of the Auditory Brain

Data used for paper Higgins, I., Stringer, S. and Schnupp, J. (2016). Unsupervised Learning of Temporal Features for Word Categorization in a Spiking Neural Network Model of the Auditory Brain. PLOS ONE (in revision)
The data include:1. Spike rasters produced by the AN-CN-IC-CX and AN-CX models in response to four different presentations of the two words "one" and "two" pronounced by 94 different speakers 2. Distribution of delays for the AN/IC -> A1 and A1 -> Belt connections3. Distribution of weights for all between layer connections (AN-PL, AN-CH, AN-ON, PL-IC, CH-IC, ON-IC, IC/AN-A1, A1-Belt)