# A cell-type specific explanation of visual decision circuits.

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $386,800

## Abstract

Abstract
The main goal of my laboratory is to understand the neural circuits that support perceptual decision-making.
Recent efforts to understand microcircuits within decision-making areas have been fruitful, thanks in part to the
ability to identify and manipulate distinct types of inhibitory neurons. Our understanding how distinct types of
excitatory pyramidal neurons (PyNs) shape circuits has lagged behind. This lack of understanding is particularly
problematic for decision circuits in which PyN type can determine the long-range project target of neurons within
an area. To surmount this problem, we propose to compare the contribution of distinct PyN types to key decision-
making computations: working memory and evidence accumulation. We will measure and manipulate neural
activity of intratelencephalic (IT) and pyramidal tract (PT) neurons in animals trained to make decisions about
the stochastically-varying spatial position of visual gratings. We seek to understand how PT and IT neurons in
multiple neural structures collectively support the computations needed for decision-making. In Aim 1, we will
use widefield calcium imaging to generate cortex-wide activity maps for each PyN type. We will compare the
spatial and temporal patterns at multiple scales, and will deploy quantitative analyses to identify candidate
regions for decision-making computations. In Aim 2, we will measure the activity of single cells within these
regions using 2-photon imaging. We will use a generalized linear model (GLM) to estimate the extent to which
single neurons are modulated by relevant variables, such as stimulus events, choice, and movements. We will
compare these across PT and IT neurons. The same models will estimate the magnitude and time course of
“coupling” between neurons, which we will compare across PyN types and regions. Shorter coupling time
courses are expected for neurons that support sensory encoding, while longer coupling time courses are
expected for neurons that support working memory. We will use these observations to generate a new,
biologically realistic model that includes PyN types in multiple areas working together to support decision-making
computations. In Aim 3, we will evaluate a causal role for PyN types using cell-type specific optogenetic
inactivation. We will compare multiple aspects of decision-making behavior on trials with vs. without inactivation.
For example, we will determine how inactivation affects decision accuracy, and the extent to which this depends
on when inactivation occurs. We will also determine how inactivation impacts the animal’s ability to accumulate
sensory evidence and/or drives movements that can be detected with a classifier trained on video data. We will
use this data to “stress test” the model, evaluating whether inactivations in our artificial network generate the
same changes as inactivations in the real brain. If not, we will update and re-test the model, creating a tight loop
between experiments and m...

## Key facts

- **NIH application ID:** 10735026
- **Project number:** 2R01EY022979-11A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** ANNE KATHRYN CHURCHLAND
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $386,800
- **Award type:** 2
- **Project period:** 2013-03-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10735026

## Citation

> US National Institutes of Health, RePORTER application 10735026, A cell-type specific explanation of visual decision circuits. (2R01EY022979-11A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10735026. Licensed CC0.

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