# Computational maps in extrastriate cortex

> **NIH NIH R21** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $187,030

## Abstract

Abstract
Studies of the visual system are taking large steps toward a general understanding of how sensory neurons
are wired to encode the outside world. A visual scene is first processed by the retina and thalamus for reliable
identification of basic features across a broad range of natural conditions, an impressive feat that requires a
complex network. From there, visual information propagates through multiple stages of cortex to build up a
neural representation that is more in-tuned with complex features of the natural world. However, we are
missing the basic physiological principles underling this visual hierarchy. Footing for this problem may reside in
the finding that basic anatomical architecture is repeated within the neocortex, which implies that discoveries in
each visual cortical area have broad implications for normal visual perception, along with cortical-based
pathologies. Our lab uses novel imaging and genetic tools to probe the mechanisms of a single cortico-cortical
processing stage: V1-to-V2. Cortical area V2 receives the majority of its input from cortical area V1, where
many labs, such as ours, have been fruitful at characterizing responses to simple visual stimuli. Taken
together, V2 provides a strong handle to understand cortical processing since its responses can be interpreted
as computations on a fairly well characterized set of inputs, V1. The goal of this two-year project is to
determine if visual computations in V2 are segregated across its surface to form a “computational map”, which
would provide major experimental leverage to study the underlying mechanisms. This hypothesis is supported
by previous studies showing that V2 compartments have unique functional signatures, albeit not
“computational”, in that they are simply inherited from V1. Testing for a computational map will be done in a
novel experimental preparation: We will simultaneously image V1 and V2 activity with calcium imaging, which
will allow for a rigorous assessment of V1-to-V2 transformations at each cortical location, with cellular
resolution. Our lab is uniquely suited to accomplish the necessary combination of high-resolution imaging and
quantitative analysis. Aim 1 will test how linear receptive fields in V1 tile the visual scene, which is necessary to
understand coding limitations of the V1 population, along with interpretations of how V1 is affecting its
downstream target, V2. Next, Aim 2 will directly test for V1-to-V2 computations by comparing V1 and V2
responses to more complex visual stimuli, consisting of the superposition of multiple stimulus elements. Our
innovative approach has the potential to answer major scientific questions, while also laying important
groundwork to further investigate the details of V1-to-V2 circuitry - Future experiments will use advanced
genetic tools to test hypotheses about the specific physiological mechanisms giving rise to cortical
computation. A physiological model of human perception and behavior will ultim...

## Key facts

- **NIH application ID:** 9851872
- **Project number:** 5R21EY029849-02
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Ian Michael Nauhaus
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $187,030
- **Award type:** 5
- **Project period:** 2019-02-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9851872

## Citation

> US National Institutes of Health, RePORTER application 9851872, Computational maps in extrastriate cortex (5R21EY029849-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9851872. Licensed CC0.

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