Michael Stryker
University of California-San Francisco, CA·Neuroscience
When we look at the world, large networks of neurons fire in complex, individual patterns. How do these patterns across the brain represent what the eyes see?
- Fields
Neuroscience
Computer Science
Mathematics & Statistics
- Topics
visual cortex
retina
neural population coding
- Grants
- 1 known grant, through Aug 2030
What they work on
- Record large populations of neurons with multi-electrode arrays in the retina and visual cortex of awake, behaving mice while they view naturalistic visual flow and simpler artificial patterns.
- Build encoding manifolds, maps of how neuron groups represent visual input, by combining manifold inference, tensor decomposition and adaptive kernel estimation.
- Compare the structure of biological encoding manifolds with those of deep convolutional neural networks to test how well these AI models capture visual processing in the brain.
More about the research
This collaborative project measures the activity of large numbers of neurons in different parts of the mouse visual system while the mouse views carefully controlled images. The data sets are enormous, so the team uses machine learning to make sense of them. The work could also show how the brain solves problems that engineers face in building artificial intelligence (AI).
Why it matters
Lessons from how the brain represents visual scenes could help engineers build AI systems that are more capable and efficient. The work could also clarify how the many kinds of neurons in the visual system cooperate to process information. This adds basic understanding of how vision works in the brain.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Tai Sing Lee
Carnegie Mellon University, PA·Neuroscience
Develop a machine learning framework that formalizes the rapid recurrent plasticity, or fast rewiring, of local circuits in the early visual cortex.
Adam Kohn
Albert Einstein College of Medicine, NY·Neuroscience
Test whether feedforward–feedback loops between visual areas carry out predictive coding, by analyzing population responses recorded at the same time in macaque V1/V2 and V1/V4 to targeted visual stimuli.
Carlos Ramon Ponce
Harvard University, MA·Neuroscience·Early-career
Compare competing ways of predicting how inferotemporal cortex neurons respond to natural images, such as hand-designed features, distance from a neuron's most activating image, and fitted neural networks, in a 'tournament' using chronic microelectrode arrays in macaques.
The school
University of California-San Francisco: 89 labs on PI Pages, $2.1B a year on research, 164 research doctorates a year.
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