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High Dimensional Geometry Visual

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Seminar✓ Recording AvailableNeuroscience

High-dimensional geometry of visual cortex

Carsen Stringer_

Dr.

Janelia Research Campus

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Wednesday, June 24, 2020

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Wednesday, June 24, 2020

4:00 PM Asia/Jerusalem

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Host: NeuroMath

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Abstract

Interpreting high-dimensional datasets requires new computational and analytical methods. We developed such methods to extract and analyze neural activity from 20,000 neurons recorded simultaneously in awake, behaving mice. The neural activity was not low-dimensional as commonly thought, but instead was high-dimensional and obeyed a power-law scaling across its eigenvalues. We developed a theory that proposes that neural responses to external stimuli maximize information capacity while maintaining a smooth neural code. We then observed power-law eigenvalue scaling in many real-world datasets, and therefore developed a nonlinear manifold embedding algorithm called Rastermap that can capture such high-dimensional structure.

Topics

eigenvalueshigh-dimensional datasetsinformation capacitymathneural activityneural codenonlinear manifold embeddingpower-law scalingrastermaptheoryvisual cortex

About the Speaker

Carsen Stringer_

Dr.

Janelia Research Campus

Contact & Resources

Personal Website

www.gatsby.ucl.ac.uk/~cstringer/

@computingnature

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twitter.com/computingnature

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