Next generation mechanistic models of retinal interneurons

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.