Title
Feature Extraction From Spectro-Temporal Signals Using Dynamic Synapses, Recurrency, And Lateral Inhibition
Abstract
This paper presents a spiking neural network-based investigation of the issues associated with extraction of onset, offset, and coincidental firing features from spectro-temporal data. Speech samples containing spoken isolated digits from the TI46 database are employed to demonstrate the way in which these features can be extracted using leaky integrate-and-fire spiking neurons with dynamic synapses. The flexibility that the additional synaptic parameters in the neuron model provides, is demonstrated to be essential for onset, offset and coincidental firing extraction. Recurrency and the interaction between excitation and inhibition together with latency is demonstrated to be a viable means of extracting offset features.The effects of lateral inhibition and in particular its ability to induce transient synchrony in spike firing is evaluated. In particular, by defining a connection length parameter, and hence a neighbourhood size, synchronous firing is shown to gradually develop as connection length and neighbourhood size increases. Finally, the implications for this connectivity in spiking neural networks and its potential for learning spectral and spatio-temporal patterns via the formation of receptive fields is discussed.
Year
DOI
Venue
2010
10.1109/IJCNN.2010.5596818
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010
Keywords
Field
DocType
temporal data,receptive field,spectrogram,neuron model,feature extraction,spiking neural network,tin,lateral inhibition,speech processing,neural nets
Receptive field,Speech processing,Biological neuron model,Pattern recognition,Computer science,Feature extraction,Lateral inhibition,Artificial intelligence,Artificial neural network,Spiking neural network,Machine learning,Offset (computer science)
Conference
ISSN
Citations 
PageRank 
2161-4393
3
0.48
References 
Authors
3
3
Name
Order
Citations
PageRank
Cornelius Glackin1528.47
Liam P. Maguire251151.18
Liam Mcdaid327030.48