Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation
Identifieur interne : 002A19 ( Ncbi/Checkpoint ); précédent : 002A18; suivant : 002A20Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation
Auteurs : Jesús A. Garrido [Italie] ; Niceto R. Luque [Espagne] ; Egidio D'Angelo [Italie] ; Eduardo Ros [Espagne]Source :
- Frontiers in Neural Circuits [ 1662-5110 ] ; 2013.
Abstract
Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr,
Url:
DOI: 10.3389/fncir.2013.00159
PubMed: 24130518
PubMed Central: 3793577
Affiliations:
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PMC:3793577Le document en format XML
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<author><name sortKey="Luque, Niceto R" sort="Luque, Niceto R" uniqKey="Luque N" first="Niceto R." last="Luque">Niceto R. Luque</name>
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<author><name sortKey="Ros, Eduardo" sort="Ros, Eduardo" uniqKey="Ros E" first="Eduardo" last="Ros">Eduardo Ros</name>
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<series><title level="j">Frontiers in Neural Circuits</title>
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<front><div type="abstract" xml:lang="en"><p>Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, <xref ref-type="bibr" rid="B65">1969</xref>
). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., <xref ref-type="bibr" rid="B37">2001</xref>
; Gao et al., <xref ref-type="bibr" rid="B34">2012</xref>
) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably <italic>self-adapted</italic>
to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a <italic>time correlator</italic>
between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the <italic>gain controller</italic>
. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario.</p>
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