Simulating the shaping of the fastigial deep nuclear saccade command by cerebellar Purkinje cells.

Early lesion and physiological studies established the key contributions of the cerebellar cortex and fastigial deep nuclei in maintaining the accuracy of saccades. Recent evidence has demonstrated that fastigial oculomotor region cells (FORCs) provide commands that are critical both for driving and braking saccades. Modeling studies have largely ignored the mechanisms by which the FORC activity patterns, and those of the Purkinje cells (PCs) that inhibit them, are produced by the mossy fiber (MF) inputs common to both. We have created a hybrid network of integrate-and-fire and summation units to model the circuitry between PCs, FORCs, and MFs that can account for all observed PC and FORC activity patterns. The model demonstrates that a crucial component of FORC activity may be due to the rebound depolarization intrinsic to FORC neurons that, like the MF-driven activity of FORCs, is also shaped by PC inhibition and disinhibition. The model further demonstrates that the shaping of the FORC saccade command by PCs can be adaptively modified through plausible learning rules based on cerebellar long-term depression (LTD) and long-term potentiation (LTP), which are guided by climbing fiber (CF) input to PCs that realistically indicates only the direction (but not the magnitude) of saccade error. These modeling results provide new insights into the adaptive control by the cerebellum of the deep nuclear saccade command.

Sparse cerebellar innervation can morph the dynamics of a model oculomotor neural integrator.

The oculomotor integrator is a brainstem neural network that converts velocity signals into the position commands necessary for eye-movement control. The cerebellum can independently adjust the amplitude of eye-movement commands and the temporal characteristics of neural integration, but the percentage of integrator neurons that receive cerebellar input is very small. Adaptive dynamic systems models, configured using the genetic algorithm, show how sparse cerebellar inputs could morph the dynamics of the oculomotor integrator and independently adjust its overall response amplitude and time course. Dynamic morphing involves an interplay of opposites, in which some model Purkinje cells exert positive feedback on the network, while others exert negative feedback. Positive feedback can be increased to prolong the integrator time course at virtually any level of negative feedback. The more these two influences oppose each other, the larger become the response amplitudes of the individual units and of the overall integrator network.