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Francis JT, Rozenboym A, von Kraus L, Xu S, Chhatbar P, Semework M, Hawley E, Chapin J. Similarities Between Somatosensory Cortical Responses Induced via Natural Touch and Microstimulation in the Ventral Posterior Lateral Thalamus in Macaques. Front Neurosci 2022; 16:812837. [PMID: 35250454 PMCID: PMC8888535 DOI: 10.3389/fnins.2022.812837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/24/2022] [Indexed: 11/30/2022] Open
Abstract
Lost sensations, such as touch, could be restored by microstimulation (MiSt) along the sensory neural substrate. Such neuroprosthetic sensory information can be used as feedback from an invasive brain-machine interface (BMI) to control a robotic arm/hand, such that tactile and proprioceptive feedback from the sensorized robotic arm/hand is directly given to the BMI user. Microstimulation in the human somatosensory thalamus (Vc) has been shown to produce somatosensory perceptions. However, until recently, systematic methods for using thalamic stimulation to evoke naturalistic touch perceptions were lacking. We have recently presented rigorous methods for determining a mapping between ventral posterior lateral thalamus (VPL) MiSt, and neural responses in the somatosensory cortex (S1), in a rodent model (Choi et al., 2016; Choi and Francis, 2018). Our technique minimizes the difference between S1 neural responses induced by natural sensory stimuli and those generated via VPL MiSt. Our goal is to develop systems that know what neural response a given MiSt will produce and possibly allow the development of natural “sensation.” To date, our optimization has been conducted in the rodent model and simulations. Here, we present data from simple non-optimized thalamic MiSt during peri-operative experiments, where we used MiSt in the VPL of macaques, which have a somatosensory system more like humans, as compared to our previous rat work (Li et al., 2014; Choi et al., 2016). We implanted arrays of microelectrodes across the hand area of the macaque S1 cortex as well as in the VPL. Multi and single-unit recordings were used to compare cortical responses to natural touch and thalamic MiSt in the anesthetized state. Post-stimulus time histograms were highly correlated between the VPL MiSt and natural touch modalities, adding support to the use of VPL MiSt toward producing a somatosensory neuroprosthesis in humans.
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Affiliation(s)
- Joseph Thachil Francis
- Cullen College of Engineering, Department of Biomedical Engineering and Electrical and Computer Engineering, University of Houston, Houston, TX, United States
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
- *Correspondence: Joseph Thachil Francis,
| | - Anna Rozenboym
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
- Department of Biological Sciences, Kingsborough Community College-CUNY, Brooklyn, NY, United States
| | - Lee von Kraus
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
| | - Shaohua Xu
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
| | - Pratik Chhatbar
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
- Department of Neurology, Duke University School of Medicine, Durham, NC, United States
| | - Mulugeta Semework
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
| | - Emerson Hawley
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
| | - John Chapin
- Department of Physiology and Pharmacology, State of New York Downstate Medical School, Brooklyn, NY, United States
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Abstract
When standard optimization methods fail to find a satisfactory solution for a parameter fitting problem, a tempting recourse is to adjust parameters manually. While tedious, this approach can be surprisingly powerful in terms of achieving optimal or near-optimal solutions. This paper outlines an optimization algorithm, Adaptive Stochastic Descent (ASD), that has been designed to replicate the essential aspects of manual parameter fitting in an automated way. Specifically, ASD uses simple principles to form probabilistic assumptions about (a) which parameters have the greatest effect on the objective function, and (b) optimal step sizes for each parameter. We show that for a certain class of optimization problems (namely, those with a moderate to large number of scalar parameter dimensions, especially if some dimensions are more important than others), ASD is capable of minimizing the objective function with far fewer function evaluations than classic optimization methods, such as the Nelder-Mead nonlinear simplex, Levenberg-Marquardt gradient descent, simulated annealing, and genetic algorithms. As a case study, we show that ASD outperforms standard algorithms when used to determine how resources should be allocated in order to minimize new HIV infections in Swaziland.
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