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Novel experimental and machine learning - assisted techniques to assess receptive field functionality in the retina

Award Number: R01EY035293
ORGANIZATION: NATIONAL EYE INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2023
PERIOD OF PERFORMANCE END DATE: 01/31/2027

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Novel experimental and machine learning - assisted techniques to assess receptive field functionality in the retina - PROJECT SUMMARY In the mouse retina, about 40 types of retinal ganglion cells (RGCs) communicate visual information to the rest of the brain. A great deal of processing takes place before RGCs send their output downstream. Some RGCs respond selectively to a narrow range of shapes, contrasts, and directions of motion or prefer localized stimuli that move differentially from their surroundings. These computations are supported by interactions between more than a hundred interneurons whose interactions give rise to the receptive fields (RFs) that describe the relationship between the stimulus to the response of the RGC. However, despite significant recent advances in the field, we still do not know what visual features are detected by the majority of RGC types. One obstacle to progress is current techniques to study RF composition, which either require prolonged recording sessions, challenging experimental techniques, or fail to detect crucial RF components. We are also limited in the conceptual understanding of how neural circuit organization translates to function and what RF motifs give rise to specific visual computations. In this proposal, we will take an innovative approach that combines machine learning techniques, biophysically realistic modeling, electrophysiology, and glutamate / calcium imaging to develop a comprehensive description of the visual abilities of multiple RGC types in complex visual scenes that is grounded in empirical data. The proposed research will substantially advance our understanding of basic and advanced response characteristics of visually active cells, opening new horizons in the examination of neuronal function in and beyond the retina.


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 = $416,684 )
2026	2026	THE REGENTS OF THE UNIVERSITY OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Vision Research	000	4	1/27/2026	NON-COMPETING CONTINUATION	$416,684
														Subtotal = $416,684

Issue Date FY: 2025 ( Subtotal = $469,367 )
2025	2025	THE REGENTS OF THE UNIV. OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Vision Research	000	3	1/24/2025	NON-COMPETING CONTINUATION	$469,367
														Subtotal = $469,367

Issue Date FY: 2024 ( Subtotal = $532,806 )
2024	2024	THE REGENTS OF THE UNIVERSITY OF COLORADO	13001 E 17TH PL STE F428	AURORA	CO	80045	ADAMS	USA	Vision Research	000	2	2/21/2024	NON-COMPETING CONTINUATION	$532,806
														Subtotal = $532,806

Issue Date FY: 2023 ( Subtotal = $476,734 )
2023	2023	REGENTS OF THE UNIVERSITY OF COLORADO, THE	13001 E 17TH PLACE F428	AURORA	CO	80045	ADAMS	USA	Vision Research	000	1	8/24/2023	NEW	$476,734
														Subtotal = $476,734

Grand Total All Awards = $1,895,591

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Novel experimental and machine learning - assisted techniques to assess receptive field functionality in the retina

Award Number: R01EY035293

ORGANIZATION: NATIONAL EYE INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/01/2023

PERIOD OF PERFORMANCE END DATE: 01/31/2027

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