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Using ConvNets, MALIS and crowd-sourcing to map the retinal connectome

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Neural circuits in the brain are formed from neurons connecting to one another in highly structured ways. However, technological limitations have prevented us from knowing much about the nature of neural connectivity and how it relates to neural computation. We have developed new technology based on 3d electron microscopy, computational image analysis and crowd-sourcing to reconstruct complete wiring diagrams for all the neurons in a piece of brain tissue.

We have densely reconstructed 950 neurons in the inner plexiform layer of the mouse retina using a combination of machine learning algorithms and human proof-reading. These reconstructions yield hints of the principles underlying neural connectivity and neural computation in the retina. I will briefly describe these results and present the computational methods leading to this work. Our machine learning method for image segmentation is a deep convolutional neural network (ConvNet), which when combined with the novel global cost function for image segmentation (MALIS) yields neuron tracing accuracy approaching that of a single human expert (tracings from multiple human experts are usually combined and proofread to increase tracing accuracy).

Joint work with Viren Jain, Moritz Helmstaedter, Kevin Briggman, Winfried Denk and Sebastian Seung.

This talk is part of the Microsoft Research Cambridge, public talks series.

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