bims-climfi 2020-09-06 papers

Issue of 2020‒09‒06
two papers selected by
Jun Maruta
Mount Sinai Health System

Nat Commun. 2020 08 31. 11(1): 4217

Molecular layer interneurons in the cerebellum encode for valence in associative learning.

Ma M, Futia GL, de Souza FMS, Ozbay BN, Llano I, Gibson EA, Restrepo D.

The cerebellum plays a crucial role in sensorimotor and associative learning. However, the contribution of molecular layer interneurons (MLIs) to these processes is not well understood. We used two-photon microscopy to study the role of ensembles of cerebellar MLIs in a go-no go task where mice obtain a sugar water reward if they lick a spout in the presence of the rewarded odorant and avoid a timeout when they refrain from licking for the unrewarded odorant. In naive animals the MLI responses did not differ between the odorants. With learning, the rewarded odorant elicited a large increase in MLI calcium responses, and the identity of the odorant could be decoded from the differential response. Importantly, MLIs switched odorant responses when the valence of the stimuli was reversed. Finally, mice took a longer time to refrain from licking in the presence of the unrewarded odorant and had difficulty becoming proficient when MLIs were inhibited by chemogenetic intervention. Our findings support a role for MLIs in learning valence in the cerebellum.

DOI: https://doi.org/10.1038/s41467-020-18034-2

Neuroscience. 2020 Aug 28. pii: S0306-4522(20)30544-3. [Epub ahead of print]

The rules of cerebellar learning: around the Ito hypothesis.

Lisberger SG.

As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning systems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the magnitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.

Keywords: cerebellum; climbing fiber; floccular complex; long-term depression; motor learning; smooth pursuit eye movements

DOI: https://doi.org/10.1016/j.neuroscience.2020.08.026