Cerebellar circuits for locomotor learning in space and time

Informazioni relative al progetto

LOCOLEARN

ID dell’accordo di sovvenzione: 866237

DOI

10.3030/866237

Progetto chiuso

Data della firma CE 10 Aprile 2020

Data di avvio 1 Maggio 2020

Data di completamento 30 Aprile 2026

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 1 999 375,00

Contributo UE

€ 1 999 375,00

Coordinato da

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)

Periodo di rendicontazione: 2023-05-01 al 2024-10-31

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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.