Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014)

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Accession:167414
" ... We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. ..."
Reference:
1 . Casellato C, Antonietti A, Garrido JA, Carrillo RR, Luque NR, Ros E, Pedrocchi A, D'Angelo E (2014) Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network. PLoS One 9:e112265 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje cell; Cerebellum interneuron granule cell; Cerebellum golgi cell;
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Simulation Environment: EDLUT;
Model Concept(s): Detailed Neuronal Models; Synaptic Plasticity; Long-term Synaptic Plasticity; Action Potentials; Learning; STDP;
Implementer(s): D'Angelo, Egidio [dangelo at unipv.it]; Garrido, Jesus A [jesus.garrido at unipv.it]; Luque, Niceto R. [nluque at ugr.es];
Search NeuronDB for information about:  Cerebellum Purkinje cell; Cerebellum interneuron granule cell;