Dmitri (Mitya) Chklovskii
Will talk about: Can connectomics help us understand neural computation? Insights from the fly visual system
Dmitri “Mitya” Chklovskii is an internationally recognized inter-disciplinary scientist with contributions to neuroscience, physics, engineering, and computer science. He studied physics and engineering in St. Petersburg, Russia, then obtained a PhD in theoretical physics from MIT in 1994. After being a Junior Fellow at the Harvard Society of Fellows he switched to theoretical neuroscience and was a Sloan Fellow at the Salk Institute. In 1999, he founded a theoretical neuroscience group at Cold Spring Harbor Laboratory, where he was an Assistant and then Associate Professor. In 2007 he moved to Janelia Farm Research Campus of the Howard Hughes Medical Institute as a Group Leader. Chklovskii’s research is aimed at reverse engineering the brain by reconstructing connectomes and developing a theory of neural computation.
Animal behavior arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. We developed a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our identification of cell types involved in motion detection allowed targeting of extremely demanding electrophysiological recordings by other labs. Preliminary results from such recordings show time delays confirming our findings. This demonstrates that connectomes can provide key insights into neuronal computations.