Learning a Peripersonal Space Representation as a Visuo-Tactile Prediction Task

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00315236" target="_blank" >RIV/68407700:21230/17:00315236 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-68600-4_13" target="_blank" >http://dx.doi.org/10.1007/978-3-319-68600-4_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-68600-4_13" target="_blank" >10.1007/978-3-319-68600-4_13</a>

Result language

angličtina

Original language name

Learning a Peripersonal Space Representation as a Visuo-Tactile Prediction Task

Original language description

The space immediately surrounding our body, or peripersonal space, is crucial for interaction with the environment. In primate brains, specific neural circuitry is responsible for its encoding. An important component is a safety margin around the body that draws on visuo-tactile interactions: approaching stimuli are registered by vision and processed, producing anticipation or prediction of contact in the tactile modality. The mechanisms of this representation and its development are not understood. We propose a computational model that addresses this: a neural network composed of a Restricted Boltzmann Machine and a feedforward neural network. The former learns in an unsupervised manner to represent position and velocity features of the stimulus. The latter is trained in a supervised way to predict the position of touch (contact). Unique to this model, it considers: (i) stimulus position and velocity, (ii) uncertainty of all variables, and (iii) not only multisensory integration but also prediction.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GJ17-15697Y" target="_blank" >GJ17-15697Y: Robot self-calibration and safe physical human-robot interaction inspired by body representations in primate brains</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2017, Part I

ISBN

978-3-319-68599-1

ISSN

0302-9743

e-ISSN

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Number of pages

9

Pages from-to

101-109

Publisher name

Springer, Cham

Place of publication

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Event location

Alghero

Event date

Sep 11, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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