Cerebellar circuits for locomotor learning in space and time

Project Information

LOCOLEARN

Grant agreement ID: 866237

DOI

10.3030/866237

Project closed

EC signature date 10 April 2020

Start date 1 May 2020

End date 30 April 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 999 375,00

EU contribution

€ 1 999 375,00

Coordinated by

FUNDACAO D. ANNA DE SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Portugal

Periodic Reporting for period 3 - LOCOLEARN (Cerebellar circuits for locomotor learning in space and time)

Reporting period: 2023-05-01 to 2024-10-31

Summary of the context and overall objectives of the project

Every movement we make requires us to coordinate our actions precisely in space and time. This proposal aims to understand how that remarkable coordination is achieved by neural circuits controlling movement. The cerebellum plays a critical role in keeping movements calibrated and coordinated, and it is thought to do this in part through a motor learning process in which predictable perturbations of movement are gradually compensated. Cerebellum-dependent forms of motor learning have been identified for a variety of behaviors, including locomotion, and locomotor learning is used as a rehabilitative therapy in human patients. We recently established locomotor learning in mice, using a custom-built, transparent split-belt treadmill that controls the speeds of the two sides of the body independently and allows for high-resolution behavioral readouts. Here, we will combine quantitative analysis of locomotor behavior with genetic circuit dissection to answer two fundamental questions: How are cerebellar outputs read out by downstream circuits, to calibrate spatial and temporal components of movement? and How are instructive signals for spatial and temporal learning encoded by cerebellar inputs? These studies will allow us to bridge levels of analysis to understand how cerebellar learning mechanisms convert behaviorally-relevant sensorimotor error signals into calibration signals that ensure accurate and coordinated movements in space and time for a wide range of behaviors.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Supervised learning depends on instructive signals that shape the output of neural circuits to support learned changes in behavior. We published a preprint (Silva et al., bioRxiv 2022) in which we used cell-type specific optogenetic perturbations activity in specific cerebellar cell types to systematically evaluate their contributions to delay eyeblink conditioning, a form of cerebellum-dependent learning. Our findings reveal a necessary and sufficient role for climbing fibers and corresponding Purkinje cell complex spike-events as instructive signals for associative cerebellar learning. This work provides some of the strongest evidence to date linking activity in specific cell types with behavioral learning.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In ongoing work we are extending our analyses of instructive signals for cerebellar learning (Silva et al., bioRxiv 2022) to their role in achieving and maintaining coordinated walking patterns in response to changes in the environment. These studies involve measuring and manipulating activity in defined cerebellar cell types, as well as their inputs and outputs, during complex, whole-body locomotor behaviors. Ongoing and future experiments will: 1) Use circuit tracing combined with manipulation of specific cerebellar outputs to identify downstream pathways for spatial and temporal locomotor learning, 2) Investigate the necessity and sufficiency of climbing fiber instructive signals for cerebellar learning via optogenetic perturbation of their activity, and 3) Image complex spike activity from populations of Purkinje cells during locomotion and learning to ask how spatial and temporal error signals are encoded within the cerebellum. These studies will allow us to bridge levels of analysis and begin to understand how cerebellar learning mechanisms convert behaviorally-relevant error signals into calibration signals that ensure accurate and coordinated movements in space and time for a wide range of behaviors.