Descripción del proyecto
Un análisis más profundo de la las células amacrinas en los cálculos visuales naturales
Una de las cuestiones más complicadas de la neurociencia visual es la enorme diversidad de las células amacrinas, una clase de interneuronas inhibidoras de la retina de los vertebrados. A pesar de su importancia, estas células siguen siendo una de las clases de neuronas de la retina que menos se conocen. Para comprender mejor su papel en los cálculos neuronales, se han desarrollado varios modelos de neuronas con distintos niveles de realismo. En el proyecto NextMechMod, financiado con fondos europeos, se aprovecharán los avances en aprendizaje automático para crear una nueva generación de modelos mecanicistas híbridos. Estos se basarán en la integración de distintos niveles de realismo en el modelado. El equipo del proyecto también creará un conjunto de herramientas para examinar sistemáticamente el papel de las células amacrinas de la retina durante los cálculos visuales naturales.
Objetivo
Ever since the work Hodgkin and Huxley, models of neurons have been essential for our understanding of neural computations. Such models have been developed at diverse levels of realism, from linear-nonlinear cascade or black-box models to detailed compartmental models. While these approaches are commonly viewed as incompatible, they have attractive strengths from an epistemic point of view. In this project, I propose to develop a new generation of hybrid mechanistic models that reconcile these levels of modelling: they will consist of a compartmental model for the neuron of interest with inputs approximated by black-box models. I will leverage the power of these hybrid models to tackle one of the most challenging questions in visual neuroscience: the staggering diversity of amacrine cells, a major class of inhibitory interneurons in the vertebrate retina. Despite their diversity, they are the least understood class of neurons in the retina, in stark contrast to the remaining circuitry. While in mouse more than 60 types of ACs have been identified by single cell transcriptomics, only a handful has been studied at depth. I will build on the latest advances in machine learning to develop a framework for efficiently inferring the parameters of a hybrid mechanistic model. To constrain the model parameters, we will acquire two-photon calcium and voltage imaging data during natural stimulation. Further, we will extend our framework to incorporate transcriptomic information about gene expression collected via patch-seq into the inference procedure, allowing us to map the amacrine cells to genetically defined types. Thus, in this project, I propose to develop a toolset to systematically uncover the role of retinal amacrine cells during natural visual computations, and link it to its mechanistic basis, providing a path forward to solving one of the key remaining mysteries of visual neuroscience.
Ámbito científico
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-AG - HORIZON Action Grant Budget-BasedInstitución de acogida
72074 Tuebingen
Alemania