Distributed model of control of saccades by superior colliculus and cerebellum
Neural Networks - Special issue on neural control and robotics: biology and technology
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input
Biological Cybernetics
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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.