The cerebellum is a relatively small center in the nervous system that accounts around half of the existing neurons. As we previously documented, the researches from the University of Granada are taking advantage of the NeuroRobotics Platform (NRP) in order to prove how cerebellar plasticity may contribute to vestibule-ocular reflex (VOR) adaptation.
Implementing neurorobotic experiments often requires some multidisciplinary efforts as:
- Establishing a neuroscience-relevant working hypothesis.
- Implementing an avatar or robot simulator to perform the task.
- Developing the brain model with the indicated level of detail.
- Transforming brain activity -spikes- into signals that can be used by the robot and viceversa.
The NRP provides useful tools in order to facilitate most of these steps. However, the definition of complex brain models might requires the implementation of neuron and synapsis models for the brain simulation platform (NEST in our particular case). The cerebellar models that we are including involves plasticity at two different synaptic sites: the parallel fibers (PF) and the mossy fibers (MF, targeting the vestibular nuclei neurons).
Although we will go deeper into the equations (see the reference above for further details) each parallel fiber synapsis will be depressed (LTD) when a presynaptic spike occurs closely to the occurrence of a complex spike of the target Purkinje cell (PC, see figure). Similarly, the plasticity at the mossy fiber/vestibular nuclei (VN) synapsis will be driven by the inhibitory activity coming from the Purkinje neurons.
These learning rules have been previously implemented for EDLUT simulator and used for complex manipulation tasks in . The neuron and synapsis models have been released in GitHub and also as part of the NRP source code. This work in the framework of the HBP will allow researchers to demonstrate the role that plasticity at the parallel fibers and mossy fibers play in vestibule-occular reflex movements.
 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.