NDLI: Improving neural decoding in the central auditory system using bio-inspired spectro-temporal representations and a generalized bilinear model

Content Provider	IEEE Xplore Digital Library
Author	Siahpoush, S. Erfani, Y. Rode, T. Lim, H.H. Rouat, J. Plourde, E.
Copyright Year	2015
Description	Author affiliation: Dept. de Genie Electr. et de Genie Inf., Univ. de Sherbrooke, Sherbrooke, QC, Canada (Siahpoush, S.; Erfani, Y.; Rouat, J.; Plourde, E.) \|\| Dept. of Biomed. Eng., Univ. of Minnesota, Minneapolis, MN, USA (Lim, H.H.) \|\| Horzentrum Hannover GmbH, Hannover, Germany (Rode, T.)
Abstract	We study the impact of different encoding models and spectro-temporal representations on the accuracy of Bayesian decoding of neural activity recorded from the central auditory system. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e. a gammatone filter bank (GFB) and a spikegram. Signal to noise ratios between the reconstructed and original representations are used to evaluate the decoding, or reconstruction accuracy. We experimentally show that the reconstruction accuracy is best with the spikegram representation and worst with the spectrogram representation and, furthermore, that using a GBM instead of a GLM significantly increases the reconstruction accuracy. In fact, our results show that the spikegram reconstruction accuracy with a GBM fitting yields an SNR that is 3.3 dB better than when using the standard decoding approach of reconstructing a spectrogram with GLM fitting.
Starting Page	5146
Ending Page	5150
File Size	624196
Page Count	5
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319550
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spectrogram Decoding Signal to noise ratio Neurons Electrodes Accuracy Biological system modeling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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