NDLI: Dynamic nonlinear modeling of interactions between neuronal ensembles using principal dynamic modes

Content Provider	IEEE Xplore Digital Library
Author	Marmarelis, V.Z. Shin, D.C. Song, D. Hampson, R.E. Deadwyler, S.A. Berger, T.W.
Copyright Year	2011
Description	Author affiliation: Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157 USA (Hampson, R.E.; Deadwyler, S.A.) \|\| Department of Biomedical Engineering and the Biomedical Simulations Resource (BMSR), University of Southern California, Los Angeles, CA 90089 USA (Marmarelis, V.Z.; Shin, D.C.) \|\| Department of Biomedical Engineering, the Biomedical Simulations Resource and the Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089 USA (Song, D.; Berger, T.W.)
Abstract	We present a novel methodology for modeling the interactions between neuronal ensembles that utilizes the concept of Principal Dynamic Modes (PDM) and their associated nonlinear functions (ANF). This new approach seeks to reduce the complexity of the multi-input/multi-output (MIMO) model of the interactions between neuronal ensembles — an issue of critical practical importance in scaling up the MIMO models to incorporate hundreds (or even thousands) of input-output neurons. Global PDMs were extracted from the data using estimated first-order and second-order kernels and singular value decomposition (SVD). These global PDMs represent an efficient “coordinate system” for the representation of the MIMO model. The ANFs of the PDMs are estimated from the histograms of the combinations of PDM output values that lead to output spikes. For initial testing and validation of this approach, we applied it to a set of data collected at the pre-frontal cortex of a non-human primate during a behavioral task (Delayed Match-to-Sample). Recorded spike trains from Layer-2 neurons were viewed as the “inputs” and from Layer-5 neurons as the outputs. Model prediction performance was evaluated by means of computed Receiver Operating Characteristic (ROC) curves. The results indicate that this methodology may greatly reduce the complexity of the MIMO model without significant degradation of performance.
Starting Page	3334
Ending Page	3337
File Size	515018
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090904
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MIMO Neurons Kernel Brain modeling Complexity theory Predictive models Computational modeling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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