Karthik Kumaravelu1, Tucker Tomlinson2, Thierri Callier3, Joseph Sombeck4, Sliman J Bensmaia3, Lee E Miller2,4,5, Warren M Grill1,6,7,8. 1. Department of Biomedical Engineering, Duke University, Durham, NC, United States of America. 2. Deptartment of Physiology, Northwestern University, Chicago, IL, United States of America. 3. Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States of America. 4. Department of Biomedical Engineering, Northwestern University, Chicago, IL, United States of America. 5. Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America. 6. Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America. 7. Department of Neurobiology, Duke University, Durham, NC, United States of America. 8. Department of Neurosurgery, Duke University, Durham, NC, United States of America.
Abstract
OBJECTIVE: Touch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g. due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge is to generate patterns of neuronal activation that evoke interpretable percepts. We present a framework to design optimal spatiotemporal patterns of ICMS (STIM) that evoke naturalistic patterns of neuronal activity and demonstrate performance superior to four previous approaches. APPROACH: We recorded multiunit activity from S1 during a center-out reach task (from proprioceptive neurons in Brodmann's area 2) and during application of skin indentations (from cutaneous neurons in Brodmann's area 1). We implemented a computational model of a cortical hypercolumn and used a genetic algorithm to design STIM that evoked patterns of model neuron activity that mimicked their experimentally-measured counterparts. Finally, from the ICMS patterns, the evoked neuronal activity, and the stimulus parameters that gave rise to it, we trained a recurrent neural network (RNN) to learn the mapping function between the physical stimulus and the biomimetic stimulation pattern, i.e. the sensory encoder to be integrated into a neuroprosthetic device. MAIN RESULTS: We identified ICMS patterns that evoked simulated responses that closely approximated the measured responses for neurons within 50 µm of the electrode tip. The RNN-based sensory encoder generalized well to untrained limb movements or skin indentations. STIM designed using the model-based optimization approach outperformed STIM designed using existing linear and nonlinear mappings. SIGNIFICANCE: The proposed framework produces an encoder that converts limb state or patterns of pressure exerted onto the prosthetic hand into STIM that evoke naturalistic patterns of neuronal activation.
OBJECTIVE: Touch and proprioception are essential to motor function as shown by the movement deficits that result from the loss of these senses, e.g. due to neuropathy of sensory nerves. To achieve a high-performance brain-controlled prosthetic arm/hand thus requires the restoration of somatosensation, perhaps through intracortical microstimulation (ICMS) of somatosensory cortex (S1). The challenge is to generate patterns of neuronal activation that evoke interpretable percepts. We present a framework to design optimal spatiotemporal patterns of ICMS (STIM) that evoke naturalistic patterns of neuronal activity and demonstrate performance superior to four previous approaches. APPROACH: We recorded multiunit activity from S1 during a center-out reach task (from proprioceptive neurons in Brodmann's area 2) and during application of skin indentations (from cutaneous neurons in Brodmann's area 1). We implemented a computational model of a cortical hypercolumn and used a genetic algorithm to design STIM that evoked patterns of model neuron activity that mimicked their experimentally-measured counterparts. Finally, from the ICMS patterns, the evoked neuronal activity, and the stimulus parameters that gave rise to it, we trained a recurrent neural network (RNN) to learn the mapping function between the physical stimulus and the biomimetic stimulation pattern, i.e. the sensory encoder to be integrated into a neuroprosthetic device. MAIN RESULTS: We identified ICMS patterns that evoked simulated responses that closely approximated the measured responses for neurons within 50 µm of the electrode tip. The RNN-based sensory encoder generalized well to untrained limb movements or skin indentations. STIM designed using the model-based optimization approach outperformed STIM designed using existing linear and nonlinear mappings. SIGNIFICANCE: The proposed framework produces an encoder that converts limb state or patterns of pressure exerted onto the prosthetic hand into STIM that evoke naturalistic patterns of neuronal activation.
Authors: Vikash Gilja; Cindy A Chestek; Ilka Diester; Jaimie M Henderson; Karl Deisseroth; Krishna V Shenoy Journal: IEEE Trans Biomed Eng Date: 2011-01-20 Impact factor: 4.538
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