  While the original goal for developing robots is replacing humans in
dangerous and tedious tasks, the final target shall be completely mimicking the
human cognitive and motor behaviour. Hence, building detailed computational
models for the human brain is one of the reasonable ways to attain this. The
cerebellum is one of the key players in our neural system to guarantee
dexterous manipulation and coordinated movements as concluded from lesions in
that region. Studies suggest that it acts as a forward model providing
anticipatory corrections for the sensory signals based on observed
discrepancies from the reference values. While most studies consider providing
the teaching signal as error in joint-space, few studies consider the error in
task-space and even fewer consider the spiking nature of the cerebellum on the
cellular-level. In this study, a detailed cellular-level forward cerebellar
model is developed, including modeling of Golgi and Basket cells which are
usually neglected in previous studies. To preserve the biological features of
the cerebellum in the developed model, a hyperparameter optimization method
tunes the network accordingly. The efficiency and biological plausibility of
the proposed cerebellar-based controller is then demonstrated under different
robotic manipulation tasks reproducing motor behaviour observed in human
reaching experiments.
