Excitatory Cerebellar Nucleocortical Circuit Provides Internal Amplification during Associative Conditioning

By Zhenyu Gao, M. Proietti-Onori, Zhanmin Lin, M. M. ten Brinke, Henk-Jan Boele, J. Potters, T. Ruigrok, Freek E. Hoebeek, C. D. De Zeeuw. 2016

151 citations recorded by the source corpus.

Summary Closed-loop circuitries between cortical and subcortical regions can facilitate precision of output patterns, but the role of such networks in the cerebellum remains to be elucidated. Here, we characterize the role of internal feedback from the cerebellar nuclei to the cerebellar cortex in classical eyeblink conditioning. We find that excitatory output neurons in the interposed nucleus provide efference-copy signals via mossy fibers to the cerebellar cortical zones that belong to the same module, triggering monosynaptic responses in granule and Golgi cells and indirectly inhibiting Purkinje cells. Upon conditioning, the local density of nucleocortical mossy fiber terminals significantly increases. Optogenetic activation and inhibition of nucleocortical fibers in conditioned animals increases and decreases the amplitude of learned eyeblink responses, respectively. Our data show that the excitatory nucleocortical closed-loop circuitry of the cerebellum relays a corollary discharge of premotor signals and suggests an amplifying role of this circuitry in controlling associative motor learning.

Subfields: Cellular and Molecular Neuroscience, Neurology, Ophthalmology

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Citations in the corpus

• Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “Recent studies have also discovered evidence of long-range sparse feedback projections from deep cerebellar nuclei to the cerebellar input layer (Houck and Person 2014; Ankri et al., 2015; Houck and Person 2015; Gao et al., 2016; Low et al., 2018).”
• Precision of Discrete and Rhythmic Forelimb Movements Requires a Distinct Neuronal Subpopulation in the Interposed Anterior Nucleus. (2018, background): “Optogenetic studies provide evidence that the Int nucleus also controls motor learning and kinematics of the eyelid (Gao et al., 2016; Heiney et al., 2014) and mediates forelimb movements through Purkinje cell activities (Lee et al., 2015; Witter et al., 2013).”
• Computational Principles of Supervised Learning in the Cerebellum (2018, unknown): “Because climbing fibers trigger the induction of plasticity at multiple sites in the cerebellar cortex (for a review, see Gao et al. 2012), the modulation of their efficacy has important functional implications for determining how much the cerebellum learns from a given error signal (Carey & Regehr…”
• Optimal degrees of synaptic connectivity (2017, unknown): “…of the connections between parallel fibers and Purkinje cells, but unsupervised forms of plasticity have been reported for synapses from mossy fibers onto granule cells (Hansel et al., 2001; Schweighofer et al., 2001; Gao et al., 2012, 2016; D’Angelo, 2014; but see Rylkova et al., 2015).”
• Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, unknown): “While there may also be contributions from common input or cerebello-cortical feedback (Gao et al., 2016), these data indicate that pontine transmission contributes to GrC task encoding and to L5-GrC correlations.”
• Purkinje cells directly inhibit granule cells in specialized regions of the cerebellar cortex (2016, unknown): “This can allow GrC layer activation to produce a net inhibition of PCs (Dizon and Khodakhah, 2011; Gao et al., 2016).”