Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation
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Frontiers Media SA
Cerebellar nucleiLong-term synaptic plasticitygain controlLearning consolidationModeling
Garrido Alcazar, J. A., Luque, N. R., D‘Angelo, E., & Ros, E. (2013). Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation. Frontiers in Neural Circuits, 7, 159. [doi: 10.3389/fncir.2013.00159]
PatrocinadorEuropean Union (EU) CEREBNET FP7-ITN238686 REALNET FP7-ICT270434; Italian Ministry of Health to Egidio D'Angelo RF-2009-1475845
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,1969). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arma 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 schene whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn te arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral test. In particular, PF-PC plasticity operated as a time correlator betweed the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. 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.