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Using deep learning to understand computations in neural circuits with Connectome-constrained Mechanistic Models

CORDIS · observation · Publication date unknown

Using deep learning to understand computations in neural circuits with Connectome-constrained Mechanistic Models Advances in experimental techniques yield detailed wiring diagrams of neural circuits in model-systems such as the Drosophila melanogaster. How can we leverage these complex connectomes, together with targeted recordings and perturbations of neural activity, to understand how neuronal populations perform computations underlying behavior? Achieving a mechanistic understanding will require models that are consistent with connectomes and biophysical mechanisms, while also being capable of performing behaviorally relevant computations. Current models fail to address this need: Mechanistic models satisfy anatomical and biophysical constraints by design, but we lack methods for optimizing them to perform tasks. Conversely, deep learning models can be optimized to perform challenging tasks

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recordType

award

status

SIGNED

region

EU

value

1997321

unit

EUR