Srini Turaga

Simulating the brain and body of the fruit fly

Through recent advances in microscopy, we now have an unprecedented view of the brain and body of the fruit fly Drosophila melanogaster. We now know the connectivity at single neuron resolution across the whole brain. How do we translate these new measurements into a deeper understanding of how the brain processes sensory information and produces behavior? I will describe two computational efforts to model the brain and the body of the fruit fly. First, I will describe a new modeling method which makes highly accurate predictions of neural activity in the fly visual system as measured in the living brain, using only measurements of its connectivity from a dead brain [1], joint work with Jakob Macke. Second, I will describe a whole body physics simulation of the fruit fly which can accurately reproduce its locomotion behaviors, both flight and walking [2], joint work with Google DeepMind.

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Bio

Srini Turaga is a Group Leader at HHMI's Janelia Research Campus, where his lab develops computational and mechanistic models of the brain and body, with a particular focus on connectomics and neural circuit function. Trained at MIT with Sebastian Seung and later at UCL's Gatsby Unit, he pioneered the use of deep learning for large-scale image segmentation and connectomics. His group integrates machine learning with mechanistic modeling beyond neuroscience to design programmable microscopes and protein biosensors, advancing experimental approaches to interrogate neural computation.

[1] Lappalainen JK, Tschopp FD, Prakhya S, McGill M, Nern A, Shinomiya K, Takemura Sy, Gruntman E, Macke JH, and Turaga SC. Connectome-constrained networks predict neural activity across the fly visual system. Nature, 2024.
https://www.nature.com/articles/s41586-024-07939-3
[2] Vaxenburg R, Siwanowicz I, Merel J, Robie AA, Morrow C, Novati G, Stefanidi Z, Card GM, Reiser MB, Botvinick MM, Branson KM, Tassa† Y, and Turaga† SC. Whole-body simulation of realistic fruit fly loco- motion with deep reinforcement learning. bioRxiv, 2024.
https://www.biorxiv.org/content/10.1101/2024.03.11.584515