Embodiment allows biologically plausible brain models to be tested in realistic environments, receiving similar feedback as it happens in real life or behavioural experimental set-ups. By adding dynamic synapses researchers can observe the effect that behavioural adaptation plays in network state evolution and vice versa. The NeuroRobotics Platform (NRP) notably boosts the embodiment of brain models into challenging tasks, allowing the neuroscientists to skip the technical issues of implementing the simulation of the scene.
One of the nervous centres that has traditionally received more attention in neuroscience is the cerebellum. It has recurrently shown to play a critical role in learning of tasks involving temporally precise movements, and its influence in eye movement control has received frequent experimental support. Although studies from cerebellum-related patients evidence that the cerebellum is also involved in complex tasks, such as limb coordination and manipulation tasks, eye movement control involves a neural circuitry that is simpler and deeply known. However, there still remain many open questions in how the cerebellum manages to control eye movement with such an astonishing accuracy.
Researchers from the University of Granada aim to study the cerebellar role under an “embodied cognition” scenario in which the cerebellum is responsible for solving and facilitating the body interaction with the environment. To that aim, they have set a behavioural task, the vestibule-ocular reflex (VOR), a neural structure facilitating the neural interaction, the cerebellar model, and a front-end human body, the humanoid iCub robot.
In particular, two particular hypotheses are to be tested with the proposed model: (i) the VOR phase adaptation due to parallel fibre (one of the main plastic synapsis in the cerebellar cortex) plasticity , and (ii) the learning consolidation and gain adaptation in VOR experiments thanks to the deep cerebellar nuclei synaptic plasticity .
They have modelled the neural basis of VOR control to provide a mechanistic understanding of the cerebellar functionality, which plays a key role in VOR adaptation. On the one hand, this modelling work aims at cross-linking data on VOR at behavioural and neural level. Through the simulation of VOR control impairments, we will examine possible consequences on the vestibular system processing capabilities of the VOR model. This approach may provide hints, or novel hypothesis, to better interpreting experimental data gathered in VOR testing.
 Clopath, C., Badura, A., De Zeeuw, C. I., & Brunel, N. (2014). A cerebellar learning model of vestibulo-ocular reflex adaptation in wild-type and mutant mice. Journal of Neuroscience, 34(21), 7203-7215.
 Luque, N. R., Garrido, J. A., Naveros, F., Carrillo, R. R., D’Angelo, E., & Ros, E. (2016). Distributed cerebellar motor learning: a spike-timing-dependent plasticity model. Frontiers in computational neuroscience, 10.
Jesús A. Garrido, Francisco Naveros, Niceto R. Luque and Eduardo Ros. University of Granada.