  We embed a spiking cerebellar model within an adaptive real-time (RT) control
loop that is able to operate a real robotic body (iCub) when performing
different vestibulo-ocular reflex (VOR) tasks. The spiking neural network
computation, including event- and time-driven neural dynamics, neural activity,
and spike-timing dependent plasticity (STDP) mechanisms, leads to a
nondeterministic computation time caused by the neural activity volleys
encountered during cerebellar simulation. This nondeterministic computation
time motivates the integration of an RT supervisor module that is able to
ensure a well-orchestrated neural computation time and robot operation.
Actually, our neurorobotic experimental setup (VOR) benefits from the
biological sensory motor delay between the cerebellum and the body to buffer
the computational overloads as well as providing flexibility in adjusting the
neural computation time and RT operation. The RT supervisor module provides for
incremental countermeasures that dynamically slow down or speed up the
cerebellar simulation by either halting the simulation or disabling certain
neural computation features (i.e., STDP mechanisms, spike propagation, and
neural updates) to cope with the RT constraints imposed by the real robot
operation. This neurorobotic experimental setup is applied to different
horizontal and vertical VOR adaptive tasks that are widely used by the
neuroscientific community to address cerebellar functioning. We aim to
elucidate the manner in which the combination of the cerebellar neural
substrate and the distributed plasticity shapes the cerebellar neural activity
to mediate motor adaptation. This paper underlies the need for a two-stage
learning process to facilitate VOR acquisition.
