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CRCNS: Unraveling the visual system's temporal code for dynamic scene processing

Award Number: R01EY037194
ORGANIZATION: NATIONAL EYE INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2024
PERIOD OF PERFORMANCE END DATE: 07/31/2029

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CRCNS: Unraveling the visual system's temporal code for dynamic scene processing - Our brain's ability to instantly recognize an object within a visual scene is almost effortless, yet obtaining this ability in artificial visual systems has taken decades. This is because the brain's computations that transform a visual scene into a neural code remain hidden among the billions of neurons and synaptic connections that make up the human visual system. Identifying and understanding these computations is the first step in providing clinical diagnoses and treatments for diseases and disorders disrupting visual processing, ranging from transient motion sickness to neurodegenerative disorders such as posterior cortical atrophy. Such treatments may involve visual prostheses to replace or bypass damaged computations (e.g., those involved in motion processing or face detection). Decades of experiments and modeling have uncovered fundamental computations in early visual cortex (retina, LGN, V1), but our knowledge of spatial feature processing (shapes, textures, colors) and temporal processing (motion, changing perspective) in higher-order visual cortex (e.g., areas V4 and IT) remains limited. This proposed research program aims to characterize the neural computations involved in how visual cortical area V4 neurons respond to dynamic video clips. We will build a computational model that accurately predicts temporal V4 responses and interrogate this model to isolate the model circuits that govern the temporal integration of visual features. To optimize the parameters of our deep neural network model, we will combine data collection and model training in a closed loop: We train our model after each recording and choose the next video clips to present based on the model's uncertainty. In other words, we keep refining our working hypothesis---a deep neural network model---through model-guided data collection. The result of this procedure will be a large-scale dataset of temporal V4 responses to natural video clips as well as a highly-predictive computational model. We will use this model to test whether feature attention dynamically modulates V4 responses, linking temporal feature integration to behavior. Overall, this innovative closed-loop approach, requiring close interdisciplinary collaboration between experimental and computational researchers, promises to unlock the neural computations involved in spatial and temporal feature processing in higher-order visual cortex.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $431,202 )
2026	2026	CARNEGIE-MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Vision Research	000	3	8/18/2026	NON-COMPETING CONTINUATION	$431,202
														Subtotal = $431,202

Issue Date FY: 2025 ( Subtotal = $360,652 )
2025	2025	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Vision Research	000	2	9/18/2025	NON-COMPETING CONTINUATION	$360,652
														Subtotal = $360,652

Issue Date FY: 2024 ( Subtotal = $429,883 )
2024	2024	CARNEGIE MELLON UNIVERSITY	5000 FORBES AVE	PITTSBURGH	PA	15213	ALLEGHENY	USA	Vision Research	000	1	8/12/2024	NEW	$429,883
														Subtotal = $429,883

Grand Total All Awards = $1,221,737

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CRCNS: Unraveling the visual system's temporal code for dynamic scene processing

Award Number: R01EY037194

ORGANIZATION: NATIONAL EYE INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2024

PERIOD OF PERFORMANCE END DATE: 07/31/2029

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