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ABSTRACT: In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few “key” neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.
IEEE Transactions on Neural Systems and Rehabilitation Engineering 11/2011; · 3.44 Impact Factor
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ABSTRACT: Precise spatiotemporal control of the output of a network of intricately connected neurons through microstimulation is highly desirable in many neural prosthetic applications. This control, however, is challenging, in part due to the large number of unobserved variables in the system under consideration, the complexity underlying the local mechanisms of microstimulation, and the interplay between the intrinsic network structure and its dynamic response to external stimulation. In this work we use a simplified firing rate model, identified from a network of Hodgkin-Huxley (HH) type spiking Basal Ganglia (BG) neurons, to study the response of the network to patterned microstimulation, and to design effective feedback control laws to approximate a desired spatiotemporal pattern. Mathematical analysis of the simplified model using Singular Value Decomposition (SVD) suggests that the BG neural circuit under study exhibits strong spatiotemporal selectivity and only responds strongly to a range of specific spatiotemporal stimulation patterns. We use the concept of functional controllability based on SVD to evaluate the effectiveness of various combinations of stimulation sites for a given set of neurons to be controlled. The results suggest that the functional controllability is largely decided by the network connectivity and the connection strength. Finally, we demonstrate that the controller design based on the simplified model is indeed effective in driving the output neurons to follow a prescribed spatiotemporal firing pattern in the network output.
Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on; 06/2011
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ABSTRACT: One of the fundamental objectives in systems neuroscience is to precisely control the spatiotemporal firing of cortical neurons to elicit a desired pattern of activity. In this work, we study the effects of intracortical micro-stimulation on the dynamics of a basal ganglia microcircuit model, and explore the feasibility of controlling the spatiotemporal firing patterns of the population in the presence of unobserved inputs. Results from the simulation study suggest that properly designed Multiple-Input-Multiple-Output (MIMO) feedback controller can force a subpopulation of output neurons to follow a prescribed spatiotemporal firing pattern despite the presence of unobserved inputs. The accuracy of the spike timing of the controlled neural firing with respect to the reference spike trains is in the order of tens of milliseconds. Even a simplified circuit model of Hammerstein-Wiener type can help in prescreening potential stimulation sites and analyzing the nominal stability of the closed-loop system.
Decision and Control (CDC), 2010 49th IEEE Conference on; 01/2011
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ABSTRACT: Analysis of neural data recorded with implantable microelectrode arrays poses a significant challenge to the neuroscience and the neural engineering communities. The numerous signal processing and analysis steps need to be performed in order to extract the affluent amount of information in these data to understand their correlation with observed behavior. This paper summarizes our most recent effort to develop a comprehensive neural signal processing and data analysis software that incorporates standard analysis tools in addition to our in-house advanced tools. The software, referred to herein as NeuroQuestreg, is implemented using MATLAB. It has been extensively tested on simulated and experimental neural data and will be disseminated to the community in the short term.
Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on; 06/2009
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ABSTRACT: Recent research in brain machine interface (BMI) has shown that cortical implants can record and wirelessly transmit neural activity to external workstations for further processing, spike sorting, and decoding. In order to reduce complexity, bandwidth, and power consumption of such systems we introduce a miniaturized real-time spike sorting VLSI architecture that is to very low signal-to-noise ratios (SNR). This completely eliminates any external spike sorting dependencies, thus, bringing the entire system one step closer to be all integrated and fully implanted. The algorithm used in this architecture exploits three features to achieve better classification and real-time sorting: the spatial neuronal distribution across electrodes, the temporal and spectral information in the spike waveforms from individual neurons, and hardware limitations imposed by the size of the implant.
Biomedical Circuits and Systems Conference, 2008. BioCAS 2008. IEEE; 12/2008
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ABSTRACT: Brain machine interfaces (BMIs) have recently received significant attention from the neuroscience and engineering communities as a result of striking advances in monitoring, processing, and modeling brain function at multiple temporal and spatial resolutions. These advances, however, have also raised significant challenges to both communities that are becoming the focus of numerous ongoing research efforts. Broadly categorized based on their level of invasiveness, BMIs relying on implantable microelectrode arrays (MEAs) have received the most attention. This paper briefly reviews some fundamental concepts underlying the operation of MEA-based BMIs and highlights in particular the signal processing challenges faced by these systems in light of their resource-constrained operation. Finally, we summarize some of our recent progress in this area and suggest some open questions for future research.
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on; 05/2008 · 4.63 Impact Factor
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K.G. Oweiss
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ABSTRACT: Characterizing the encapsulation layer caused by glial scar formation surrounding microelectrode arrays in chronic implants has been the subject of extensive research. Typically, an equivalent circuit model is used to characterize the reactive tissue response by nonlinearly fitting the electrical impedance spectroscopy (EIS) data. This model assumes a time invariant adjacent layer of encapsulation tissue to have the same structure on every electrode site. In this paper, an alternative approach is proposed based on modeling the encapsulation layer as a time varying communication channel. The channel is characterized by a multi-input multi-output (MIMO) transfer function with time varying coefficients. This model circumvents spatial resolution limitations of existing EIS equivalent circuit models. It further allows capturing the observed changes in neural signal quality over time. We show that "equalizing" the channel using this model can yield a substantial improvement in signal quality. With tendency towards high-density electrode arrays for cortical implantation, the proposed model is better suited to equalize the fading channel and interpret the recorded signals with higher accuracy. We also show conceptually how patterned waveforms can periodically be used to probe the channel if adverse effects can be avoided. This can potentially improve the channel estimator performance, particularly when cell migration occurs
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE; 10/2006
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ABSTRACT: On chip signal compression is one of the key technologies driving development of energy efficient biotelemetry devices. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines sigma delta conversion with the spatial data compression in a single module. The architecture called multiple-input multiple-output (MIMO) sigma-delta is based on a min-max gradient descent optimization of a regularized cost function that naturally leads to an A/D formulation. Experimental results with simulated and recorded multichannel data demonstrate the effectiveness of the proposed architecture to eliminate cross-channel redundancy in high density microelectrode data, thus superceding the performance of parallel independent data converters in terms of its energy efficiency
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE; 10/2006
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ABSTRACT: This paper suggests a new approach for identifying clusters of neurons with correlated spiking activity in large-size neuronal ensembles recorded with high-density microelectrode arrays. The nonparametric approach relies on mapping the neuronal spike trains to a 'scale space' using a nested multiresolution projection. Similarity measures can be arbitrarily defined in the scale space independent of the fixed bin width classically used to assess neuronal correlation. This representation allows efficient graph partitioning techniques to be used to identify clusters of correlated firing within distinct behavioral contexts. We use a new probabilistic spectral clustering algorithm that simultaneously maximizes cluster aggregation based on similarity measures. The technique is able to efficiently identify functionally interdependent neurons regardless of the temporal scale from which rate functions are typically estimated. We report the clustering performance of the algorithm applied to a synthesized neurophysiological data set and compare it to known clustering techniques to illustrate the substantial gain in the performance
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE; 10/2006
