SOURCE-LINKED INTELLIGENCE

Next generation mechanistic models of retinal interneurons

CORDIS · observation · Publication date unknown

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. simulation-based inference, amacrine cells, multimodal data, mechanistic models, patch-seq, two-photon imaging, single cell transcriptomics, interneuron

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recordType

award

status

SIGNED

region

EU

value

1499860

unit

EUR