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Front Neurosci. 2016 Feb 9;10:28. doi: 10.3389/fnins.2016.00028. eCollection 2016.

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.

Author information

1
Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA.
2
Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA.
3
Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; BME Cullen College of Engineering, University of HoustonHouston, TX, USA.
4
Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, Kings County Hospital CenterBrooklyn, NY, USA.

Abstract

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.

KEYWORDS:

biomimetic; inverse model; kernel adaptive filtering; musculoskeletal arm; neuroprosthetics; neurostimulation; spiking network model; virtual arm

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