DocumentCode :
29038
Title :
Adaptive Inverse Control of Neural Spatiotemporal Spike Patterns With a Reproducing Kernel Hilbert Space (RKHS) Framework
Author :
Lin Li ; Il Memming Park ; Brockmeier, Austin ; Badong Chen ; Seth, Sachin ; Francis, Joseph T. ; Sanchez, J.C. ; Principe, Jose C.
Author_Institution :
Dept. of Electr. Eng., Univ. of Florida, Gainesville, FL, USA
Volume :
21
Issue :
4
fYear :
2013
fDate :
Jul-13
Firstpage :
532
Lastpage :
543
Abstract :
The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate- representation of neural signal (i.e., spikernel and generalized linear model). Moreover, after a significant perturbation of the neuron circuit, the control scheme can successfully drive the elicited responses close to the original target responses.
Keywords :
Hilbert spaces; MIMO systems; adaptive control; adaptive signal processing; bioelectric phenomena; decoding; medical control systems; medical signal processing; multidimensional signal processing; neurophysiology; nonlinear control systems; signal representation; spatiotemporal phenomena; MIMO adaptive inverse control scheme; Schoenberg kernel maps; adaptive inverse control; control procedure; decoding accuracy; electrical stimulation; elicited system response; generalized linear model; linear algorithm; multidimensional time-varying signal representation; multiple-input multiple-output adaptive inverse control scheme; neural perturbations; neural signal representation; neural spatiotemporal spike patterns; neural stimulation; neural system plasticity; neurons; nonlinear neural system control; population spiking activity; realistic synthetic neural circuit; reproducing kernel Hilbert space framework; spike train RKHS; spikernel model; spiking responses; standard control methodology; system control problem; target system response; time-invariant homogeneous model; Adaptation models; Decoding; Integrated circuit modeling; Kernel; MIMO; Timing; Vectors; Adaptive inverse control; Schoenberg kernel; neural stimulation; spike timing representations; Algorithms; Computer Simulation; Electric Stimulation; Electrophysiological Processes; Finite Element Analysis; Humans; Linear Models; Microelectrodes; Models, Neurological; Neural Networks (Computer); Neural Pathways; Neuronal Plasticity; Neurons; Regression Analysis; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1534-4320
Type :
jour
DOI :
10.1109/TNSRE.2012.2200300
Filename :
6256739
Link To Document :
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