Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
TocHeadingTitle
Date
Availability
1-1 of 1
Brian Gardner
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks
UnavailablePublisher: Journals Gateway
Neural Computation (2015) 27 (12): 2548–2586.
Published: 01 December 2015
FIGURES
| View All (32)
Abstract
View articletitled, Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks
View
PDF
for article titled, Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks
Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.