DocumentCode :
1239804
Title :
Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles
Author :
Zanos, Theodoros P. ; Courellis, Spiros H. ; Berger, Theodore W. ; Hampson, Robert E. ; Deadwyler, Sam A. ; Marmarelis, Vasilis Z.
Author_Institution :
Biomed. Eng. Dept., Univ. of Southern California, Los Angeles, CA
Volume :
16
Issue :
4
fYear :
2008
Firstpage :
336
Lastpage :
352
Abstract :
The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of ldquomultidimensionalrdquo time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials-treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the ldquoinputsrdquo into spike-trains recorded from another set of neurons designated as the ldquooutputsrdquo. The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input-output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann-Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat.
Keywords :
causality; least squares approximations; neural nets; neurophysiology; nonlinear dynamical systems; sensitivity analysis; stochastic processes; time series; Laguerre expansion; MIMO modeling methodology; MISO Volterra model; Mann-Whitney statistic; action potentials; causal interrelationships; hippocampus; least-squares method; multidimensional time-series data; multiunit recordings; neuronal dynamics; neuronal ensembles; nonlinear modeling; receiver operating characteristic curve; spike trains; Functional Connectivity; Functional connectivity; Hippocampus; Input Selection; Laguerre Expansion; Laguerre expansion; Mann–Whitney; Mann-Whitney; Multi Electrode; Multi Input; Multi Output; Nonlinear Modeling; Point Process; ROC Curves; Spikes; Volterra Series; hippocampus; input selection; multielectrode; multiinput; multioutput; nonlinear modeling; point process; receiver operating characteristic (ROC) curves; spikes; volterra series; Action Potentials; Animals; Computer Simulation; Humans; Models, Neurological; Nerve Net; Neurons; Nonlinear Dynamics; Synaptic Transmission;
fLanguage :
English
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1534-4320
Type :
jour
DOI :
10.1109/TNSRE.2008.926716
Filename :
4537157
Link To Document :
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