Descripción del proyecto
Unos métodos estadísticos podrían ayudarnos a comprender mejor el procesamiento de señales celulares en el «pequeño cerebro»
El cerebelo es esencial para afinar conductas temporalmente precisas: acciones o movimientos complejos que se programan y ejecutan en cuestión de milisegundos. La corteza cerebelosa, la parte más exterior del encéfalo, posee una estructura relativamente simple. Contribuye a la representación de información sensorial dependiente del tiempo y relevante para la ejecución y planificación motoras. El proyecto SICNET, financiado con fondos europeos, está aplicando rigurosos métodos estadísticos para mejorar la comprensión del procesamiento sináptico y la computación de circuitos en la corteza cerebelosa. A partir de la imagenología de datos de comportamientos animales, SICNET empleará métodos matemáticos para extraer la actividad, determinar la conectividad y las propiedades sinápticas de los tipos celulares conocidos y evaluar la capacidad de procesamiento de la red del cerebelo. Los métodos deberían identificar los mecanismos celulares de las computaciones de circuitos en el cerebelo y resultar muy valiosos para el estudio de otras partes del encéfalo.
Objetivo
The brain can coordinate complex sequences of actions with the accuracy of milliseconds. Where and how these neural computations occur is an open question in neuroscience. Despite recent technological developments allowing for large-scale high-resolution functional imaging of the brain and direct neuronal recordings in behaving animals, there has been little effort in applying rigorous statistical approaches to test circuit connectivity patterns and synaptic mechanisms driving neural activity.
Experimental evidence from classical conditioning and neuronal recordings have revealed that the cerebellum plays a fundamental role in fine-tuning of temporally precise behaviors. This project aims to elucidate the neural computation arising from anatomical and physiological constraints of the comparatively simple organization of the cerebellar cortical circuit, which allows the cerebellum to represent time-dependent sensory information necessary to drive behavior. Experimental and theoretical findings in the host laboratory have led to the hypothesis that dynamic synapse are a substrate for temporal representations and temporal learning. I will use sequential Monte Carlo methods to extract activity from calcium imaging data. Then I will use a generative model of the cerebellar network to infer the connectivity among the known cell types of the cerebellum as well as their synaptic properties. Finally, I will use information theory to examine the processing capacity of the cerebellar network, thereby providing new insights on evolutionary optimization of brain computation.
The combination of my experience in statistical methods and the host laboratory's experience in state-of-art neural recordings and theoretical models, is a perfect match to break down the barriers to understanding the cellular mechanisms of circuit computations. We believe that this analysis approach could also be applied to understand other neuronal circuits.
Ámbito científico
Programa(s)
Régimen de financiación
MSCA-IF-EF-ST - Standard EFCoordinador
75724 Paris
Francia