Long-term depression (LTD) at parallel fiber-Purkinje cell (PF-PC) synapses is thought to underlie memory formation in cerebellar motor learning. Recent experimental results, however, suggest that multiple plasticity mechanisms in the cerebellar cortex and cerebellar/vestibular nuclei participate in memory formation. To examine this possibility, we formulated a simple model of the cerebellum with a minimal number of components based on its known anatomy and physiology, implementing both LTD and long-term potentiation (LTP) at PF-PC synapses and mossy fibervestibular nuclear neuron (MF-VN) synapses. With this model, we conducted a simulation study of the gain adaptation of optokinetic response (OKR) eye movement. Our model reproduced several important aspects of previously reported experimental results in wild-type and cerebellum-related gene-manipulated mice. First, each 1-h training led to the formation of short-term memory of learned OKR gain at PF-PC synapses, which diminished throughout the day. Second, daily repetition of the training gradually formed long-term memory that was maintained for days at MF-VN synapses. We reproduced such memory formation under various learning conditions. Third, long-term memory formation occurred after training but not during training, indicating that the memory consolidation occurred during posttraining periods. Fourth, spaced training outperformed massed training in long-term memory formation. Finally, we reproduced OKR gain changes consistent with the changes in the vestibuloocular reflex (VOR) previously reported in some genemanipulated mice.cerebellum | plasticity | memory consolidation | posttraining period | Marr-Albus-Ito theory L ong-term depression (LTD) at parallel fiber-Purkinje cell (PF-PC) synapses in the cerebellar cortex has been thought to be the major mechanism of motor learning (1). This MarrAlbus-Ito hypothesis (2, 3), however, has been challenged since Miles and Lisberger's proposal (4) that long-term potentiation (LTP) at mossy fiber-vestibular nuclear neuron (MF-VN) synapses, not LTD at PF-PC synapses, underlies vestibuloocular reflex (VOR) gain adaptation (4-7). In a recent study on optokinetic response (OKR) gain adaptation, we found evidence that might resolve the controversy: LTD at PF-PC synapses (PF-LTD) and LTP at MF-VN synapses (MF-LTP) play different roles in OKR adaptation (8-10). Namely, PF-LTD accounts for short-term memory in PCs during 1-h training, whereas MF-LTP forms long-term memory in VN after the 1-h training that accumulates during repeated trials of 1-h training. It thus appears as if short-term memory formed in PCs during 1-h training is transferred to VN after training to consolidate as long-term memory (8-10).To investigate the mechanisms of this memory transfer and posttraining consolidation, we conducted a computer simulation study using a simple theoretical model of the cerebellovestibular system including both LTD and LTP at PF-PC synapses and MF-VN synapses. Although several theoretical models have addressed the question of ...

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While the anatomy of the cerebellar microcircuit is well-studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs) form with the molecular layer interneurons (MLIs)—the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1) spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2) adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

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Theoretical and computational models of the cerebellum typically focus on the role of parallel fiber (PF)—Purkinje cell (PKJ) synapses for learned behavior, but few emphasize the role of the molecular layer interneurons (MLIs)—the stellate and basket cells. A number of recent experimental results suggest the role of MLIs is more important than previous models put forth. We investigate learning at PF—MLI synapses and propose a mathematical model to describe plasticity at this synapse. We perform computer simulations with this form of learning using a spiking neuron model of the MLI and show that it reproduces six in vitro experimental results in addition to simulating four novel protocols. Further, we show how this plasticity model can predict the results of other experimental protocols that are not simulated. Finally, we hypothesize what the biological mechanisms are for changes in synaptic efficacy that embody the phenomenological model proposed here.