Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.

Currently little is known about how a mechanically coupled BMI system’s actions are integrated into ongoing body dynamics. We tested a locomotor task augmented with a BMI system driving a robot mechanically interacting with a rat under three conditions: control locomotion (BL), ‘simple elastic load’...

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Main Authors: Weiguo eSong, Iahn eCajigas, Emery N Brown, Simon eGiszter
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-04-01
Series:Frontiers in Systems Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnsys.2015.00062/full
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spelling doaj-0cb944703c0246ac9996e65a7160d69f2020-11-24T22:31:53ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372015-04-01910.3389/fnsys.2015.00062132919Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.Weiguo eSong0Iahn eCajigas1Emery N Brown2Simon eGiszter3Drexel Med SchoolMassachussets Institute of TechnologyMassachussets Institute of TechnologyDrexel Med SchoolCurrently little is known about how a mechanically coupled BMI system’s actions are integrated into ongoing body dynamics. We tested a locomotor task augmented with a BMI system driving a robot mechanically interacting with a rat under three conditions: control locomotion (BL), ‘simple elastic load’ (E) and ‘BMI with elastic load’ (BMI/E). The effect of the BMI was to allow compensation of the elastic load as a function of the neural drive. Neurons recorded here were close to one another in cortex, all within a 200 micron diameter horizontal distance of one another. The interactions of these close assemblies of neurons may differ from those among neurons at longer distances in BMI tasks and thus are important to explore. A point process generalized linear model (GLM), was used to examine connectivity at two different binning timescales (1ms vs. 10ms). We used GLM models to fit non-Poisson neural dynamics solely using other neurons’ prior neural activity as covariates. Models at different timescales were compared based on Kolmogorov-Smirnov (KS) goodness-of-fit and parsimony. About 15% of cells with non-Poisson firing were well fitted with the neuron-to-neuron models alone. More such cells were fitted at the 1ms binning than 10ms. Positive connection parameters (‘excitation’ ~70%) exceeded negative parameters (‘inhibition’ ~30%). Significant connectivity changes in the GLM determined networks of well-fitted neurons occurred between the conditions. However, a common core of connections comprising at least ~15% of connections persisted between any two of the three conditions. Significantly almost twice as many connections were in common between the two load conditions (~27%), compared to between either load condition and the baseline. This local point process GLM identified neural correlation structure and the changes seen across task conditions in the rats in this neural subset may be intrinsic to cortex or due to feedback and input reorganization in adaptation.http://journal.frontiersin.org/Journal/10.3389/fnsys.2015.00062/fullmotor adaptationElastic fieldAugmenting BMIPoint-process General Linear ModelExoskeleton robot model system
collection DOAJ
language English
format Article
sources DOAJ
author Weiguo eSong
Iahn eCajigas
Emery N Brown
Simon eGiszter
spellingShingle Weiguo eSong
Iahn eCajigas
Emery N Brown
Simon eGiszter
Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.
Frontiers in Systems Neuroscience
motor adaptation
Elastic field
Augmenting BMI
Point-process General Linear Model
Exoskeleton robot model system
author_facet Weiguo eSong
Iahn eCajigas
Emery N Brown
Simon eGiszter
author_sort Weiguo eSong
title Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.
title_short Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.
title_full Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.
title_fullStr Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.
title_full_unstemmed Adaptation to Elastic Loads and BMI Robot Controls During Rat Locomotion examined with Point-Process GLMs.
title_sort adaptation to elastic loads and bmi robot controls during rat locomotion examined with point-process glms.
publisher Frontiers Media S.A.
series Frontiers in Systems Neuroscience
issn 1662-5137
publishDate 2015-04-01
description Currently little is known about how a mechanically coupled BMI system’s actions are integrated into ongoing body dynamics. We tested a locomotor task augmented with a BMI system driving a robot mechanically interacting with a rat under three conditions: control locomotion (BL), ‘simple elastic load’ (E) and ‘BMI with elastic load’ (BMI/E). The effect of the BMI was to allow compensation of the elastic load as a function of the neural drive. Neurons recorded here were close to one another in cortex, all within a 200 micron diameter horizontal distance of one another. The interactions of these close assemblies of neurons may differ from those among neurons at longer distances in BMI tasks and thus are important to explore. A point process generalized linear model (GLM), was used to examine connectivity at two different binning timescales (1ms vs. 10ms). We used GLM models to fit non-Poisson neural dynamics solely using other neurons’ prior neural activity as covariates. Models at different timescales were compared based on Kolmogorov-Smirnov (KS) goodness-of-fit and parsimony. About 15% of cells with non-Poisson firing were well fitted with the neuron-to-neuron models alone. More such cells were fitted at the 1ms binning than 10ms. Positive connection parameters (‘excitation’ ~70%) exceeded negative parameters (‘inhibition’ ~30%). Significant connectivity changes in the GLM determined networks of well-fitted neurons occurred between the conditions. However, a common core of connections comprising at least ~15% of connections persisted between any two of the three conditions. Significantly almost twice as many connections were in common between the two load conditions (~27%), compared to between either load condition and the baseline. This local point process GLM identified neural correlation structure and the changes seen across task conditions in the rats in this neural subset may be intrinsic to cortex or due to feedback and input reorganization in adaptation.
topic motor adaptation
Elastic field
Augmenting BMI
Point-process General Linear Model
Exoskeleton robot model system
url http://journal.frontiersin.org/Journal/10.3389/fnsys.2015.00062/full
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