The ability of the brain to rapidly process information from multiple pathways is critical for reliable execution of complex sensory-motor behaviors, yet the cellular mechanisms underlying a neuronal representation of multimodal stimuli are poorly understood. Here we explored the possibility that the physiological diversity of mossy fiber (MF) to granule cell (GC) synapses within the mouse vestibulocerebellum may contribute to the processing of coincident multisensory information at the level of individual GCs. We found that the strength and short-term dynamics of individual MF-GC synapses can act as biophysical signatures for primary vestibular, secondary vestibular and visual input pathways. The majority of GCs receive inputs from different modalities, which when co-activated, produced enhanced GC firing rates and distinct first spike latencies. Thus, pathway-specific synaptic response properties permit temporal coding of correlated multisensory input by single GCs, thereby enriching sensory representation and facilitating pattern separation.

Lead contact: t.mrsic-flogel@ucl.ac.uk 7 8In motor neocortex, preparatory activity predictive of specific movements is maintained by a positive 9 feedback loop with the thalamus. Motor thalamus receives excitatory input from the cerebellum, 10 which learns to generate predictive signals for motor control. The contribution of this pathway to 11 neocortical preparatory signals remains poorly understood. Here we show that in a virtual reality 12 conditioning task, cerebellar output neurons in the dentate nucleus exhibit preparatory activity 13 similar to that in anterolateral motor cortex prior to reward acquisition. Silencing activity in dentate 14 nucleus by photoactivating inhibitory Purkinje cells in the cerebellar cortex caused robust, short-15 latency suppression of preparatory activity in anterolateral motor cortex. Our results suggest that 16 preparatory activity is controlled by a learned decrease of Purkinje cell firing in advance of reward 17 under supervision of climbing fibre inputs signalling reward delivery. Thus, cerebellar computations 18 exert a powerful influence on preparatory activity in motor neocortex. 19 20 21 to upcoming movements or salient events such as reward (Giovannucci et al., 2017;Huang et al., 2013; 48 Kennedy et al., 2014; Wagner et al., 2017). For instance, DN neurons exhibit ramping activity predictive 49 of the timing and direction of the self-initiated saccades (Ashmore and Sommer, 2013; Ohmae et al., 50 2017). Moreover, inactivation of IPN activity reduces persistent activity in a region of medial prefrontal 51 cortex involved in trace eyeblink conditioning (Siegel and Mauk, 2013). Finally, a recent study has 52 established the existence of a loop between ALM and the cerebellum necessary for the maintenance of 53 preparatory activity (Gao et al., 2018). These results suggest that the cerebellum participates in 54 programming future actions, but the details of how it may contribute to preparatory activity in the 55 neocortex during goal-directed behaviour remain to be determined. 56 57 RESULTS 58 59 Preparatory activity in ALM prior to reward acquisition in a virtual corridor 60We developed a visuomotor task in which mice ran through a virtual corridor comprising salient visual 61 cues to reach a defined location where a reward was delivered (80 cm from the appearance of the 62 second checkerboard pattern; Figure 1A; see Methods). Within a week of training, mice learned to 63 128The cerebellar dentate nucleus exhibits preparatory activity 129Since the DN sends excitatory projections to the motor thalamus (Guo et al., 2017;Ichinohe et al., 2000; 130 Middleton and Strick, 1997; Thach and Jones, 1979), which has been shown to participate in the 131 maintenance of preparatory activity in mouse ALM neocortex (Guo et al., 2017), we investigated 132 whether DN activity could influence ALM processing. We first recorded the activity of DN neurons to 133 determine how their activity was modulated during the task (Figure 2A). Forty four percent of all 134 recorded DN neurons (n =...