Understanding the building blocks of neural computation: Insights from connectomics and theory
Dmitri "Mitya" Chklovskii
, Janelia Farm, HHMI
Date: Thursday, October 10, 2013
Time: 11:30 AM to 12:30 PM Note: all times are in the Eastern Time Zone
Location: Singleton Auditorium, MIT Bldg 46-3002
Host: Prof. Tomaso Poggio, Prof. Sebastian Seung, Brain and Cognitive Sciences Department - MIT
Contact: Kathleen Sullivan, email@example.com
Relevant URL: http://www.hhmi.org/scientists/dmitri-b-chklovskii
Speaker URL: None
TALK: Understanding the building blocks of neural computation: Insights from connectomics and theory
This talk is part of the Brains, Minds & Machines Seminar Series 2013-2014
Abstract: Animal behaviour 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 are consistent with a correlation-based motion detector. This demonstrates that connectomes can provide key insights into neuronal computations.
Created by Kathleen Sullivan at Wednesday, September 11, 2013 at 4:37 PM.