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Using Neuronal States for Transcribing Cortical Activity into Muscular Effort

Identifieur interne : 001A09 ( Main/Merge ); précédent : 001A08; suivant : 001A10

Using Neuronal States for Transcribing Cortical Activity into Muscular Effort

Auteurs : Octave Boussaton [France] ; Laurent Bougrain [France]

Source :

RBID : Hal:hal-00762310

Abstract

We study the relations between the ac- tivity of corticomotoneuronal (CM) cells and the forces exerted by fingers. The activity of CM cells, located in the primary motor cortex is recorded in the thumb and index fingers area of a monkey. The activity of the fingers is recorded as they press two levers. The main idea of this work is to establish and use a collection of neuronal states. At any time, the neuronal state is defined by the firing rates of the recorded neurons. We assume that any such neuronal state is related to a typical variation (or absence of variation) in the muscular effort. Our forecasting model uses a linear combination of the firing rates, some synchrony information between spike trains and averaged variations of the positions of the levers.

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Hal:hal-00762310

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<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Université de Lorraine</orgName>
</affiliation>
</author>
</analytic>
</biblStruct>
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<textClass></textClass>
</profileDesc>
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<front>
<div type="abstract" xml:lang="en">We study the relations between the ac- tivity of corticomotoneuronal (CM) cells and the forces exerted by fingers. The activity of CM cells, located in the primary motor cortex is recorded in the thumb and index fingers area of a monkey. The activity of the fingers is recorded as they press two levers. The main idea of this work is to establish and use a collection of neuronal states. At any time, the neuronal state is defined by the firing rates of the recorded neurons. We assume that any such neuronal state is related to a typical variation (or absence of variation) in the muscular effort. Our forecasting model uses a linear combination of the firing rates, some synchrony information between spike trains and averaged variations of the positions of the levers.</div>
</front>
</TEI>
</record>

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