The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsib...

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Main Authors: Patrick Kasi, James Wright, Heba Khamis, Ingvars Birznieks, André van Schaik
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2016-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4831826?pdf=render
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spelling doaj-15c9ddf4874647238ffe7908caf9cba12020-11-25T01:28:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01114e015336610.1371/journal.pone.0153366The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.Patrick KasiJames WrightHeba KhamisIngvars BirznieksAndré van SchaikIt is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions--consistent with neural systems--with little computational resources. This makes it suitable for interfacing with prostheses.http://europepmc.org/articles/PMC4831826?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Patrick Kasi
James Wright
Heba Khamis
Ingvars Birznieks
André van Schaik
spellingShingle Patrick Kasi
James Wright
Heba Khamis
Ingvars Birznieks
André van Schaik
The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.
PLoS ONE
author_facet Patrick Kasi
James Wright
Heba Khamis
Ingvars Birznieks
André van Schaik
author_sort Patrick Kasi
title The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.
title_short The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.
title_full The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.
title_fullStr The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.
title_full_unstemmed The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans.
title_sort bayesian decoding of force stimuli from slowly adapting type i fibers in humans.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2016-01-01
description It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force's rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions--consistent with neural systems--with little computational resources. This makes it suitable for interfacing with prostheses.
url http://europepmc.org/articles/PMC4831826?pdf=render
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