# Flexible normalization in ferret V1: computational modeling and 2-photon imaging > **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $477,754 ## Abstract ABSTRACT The remarkable efficiency of human perception derives from the fact that we do not process each stimulus as a novel event. Instead, past experiences and scene context inform internal, working models of the world that allow us to generate predictions for our physical environment. A leading theory suggests that perceptual predictions are accomplished via flexible normalization: local inhibitory neuronal populations are regulated by long-range connections so that responses are suppressed when they do not provide helpful information about object boundaries. However, the precise neural mechanisms by which the healthy human brain accomplishes this flexible normalization are not known. In order to understand exactly how neural population responses are suppressed or enhanced in response to different scene contexts, we will perform 2-photon imaging in ferret primary visual cortex (V1) to quantify the responses of excitatory and inhibitory neural populations in superficial layers of cortex during several different visual stimulus paradigms. The ferret model is chosen because the imaging techniques necessary to quantify inhibitory neuronal responses are not yet well established in primate models, and while our current knowledge about neural morphology and connections has been derived from mouse models, mouse visual cortex lacks the “columnar organization” (spatial grouping of neurons with similar response properties) that is a hallmark of primate visual cortex and is present in ferrets. Thus, the ferret model is well-positioned to bridge the gap between mouse models and primate models. First, in order to understand neuronal behaviors in the absence of contextual modulation, we will characterize interactions within a single hypercolumn to small, simple stimuli (sinusoidally modulated luminance gratings) at a range of orientations and contrasts. We hypothesize that parvalbumin-containing (PV+) inhibitory interneurons will demonstrate the sharpest orientation tuning, followed by somatostatin-containing (SOM+) and serotonin-positive (5HTR+) populations. Next. using a Cross Orientation Suppression paradigm, we will test the hypothesis that that SOM+ responses track the overall contrast energy in the stimulus, while PV+ populations reflect suppression of individual grating component representations. Additional experiments with naturalistic textures will test whether these behaviors generalize to stimuli with a broad range of contrasts, orientations, and spatial frequencies. Finally, we will use classical Orientation-Dependent Surround Suppression and Collinear Facilitation paradigms to study how the local inhibitory pool responds to scene context. We hypothesize that the responses of local 5HTR+ neurons will reflect the surrounding stimuli rather than the center stimuli. Together, these experiments will constrain an open-source computational model articulated at the level of the single neuron that will constrain hypotheses about how human perceptual beha... ## Key facts - **NIH application ID:** 10299683 - **Project number:** 1R01NS123842-01 - **Recipient organization:** UNIVERSITY OF MINNESOTA - **Principal Investigator:** THEODEN I NETOFF - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $477,754 - **Award type:** 1 - **Project period:** 2021-08-01 → 2026-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10299683 ## Citation > US National Institutes of Health, RePORTER application 10299683, Flexible normalization in ferret V1: computational modeling and 2-photon imaging (1R01NS123842-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10299683. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*