NDLI: Identification of functional synaptic plasticity from ensemble spiking activities: A nonlinear dynamical modeling approach

Content Provider	IEEE Xplore Digital Library
Author	Dong Song Robinson, B.S. Chan, R.H.M. Marmarelis, V.Z. Hampson, R.E. Deadwyler, S.A. Berger, T.W.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., City Univ. of Hong Kong, Hong Kong, China (Chan, R.H.M.) \|\| Dept. of Physiol. & Pharmacology, Wake Forest Univ., Wake Forest, NC, USA (Hampson, R.E.; Deadwyler, S.A.) \|\| Dept. of Biomed. Eng., Univ. of Southern California, Los Angeles, CA, USA (Dong Song; Robinson, B.S.; Marmarelis, V.Z.; Berger, T.W.)
Abstract	This paper presents a systems identification approach for studying the long-term neural plasticity using natural ensemble spiking activities recorded from behaving animals. It is designed to quantify and explain the non-stationarity in the input-output properties of a brain region. Specifically, we propose a three-step strategy for such a goal. First, a multiple-input, multiple-output (MIMO) nonlinear dynamical model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MIMO model is extended to a time-varying form and used to track the non-stationary properties of functional connectivity. Finally, an ensemble synaptic learning rule is identified to explain the input-output non-stationary as the consequence of the past input-output spiking patterns. This framework can be used to study the underlying mechanisms of learning and memory in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.
Starting Page	617
Ending Page	620
File Size	976835
Page Count	4
File Format	PDF
ISBN	9781467319690
ISSN	19483554
DOI	10.1109/NER.2013.6696010
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Animals Neurons Sociology Brain modeling MIMO Nonlinear dynamical systems Kernel
Content Type	Text
Resource Type	Article

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