# Studying perceptual decision-making across cortex by combining population imaging, connectomics, and computational modeling

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2020 · $1,143,583

## Abstract

Project Summary
During perceptual decision-making, populations of neurons, arranged in highly interconnected microcircuits,
work together to encode sensory stimuli and to transform sensory perception into appropriate behavioral choices.
A fundamental gap in our knowledge about perceptual decision-making is understanding how the connectivity in
cortical microcircuits shapes dynamics and information codes in populations of neurons. This gap has arisen
because anatomical connectivity and activity have generally been studied separately, and because a
computational framework to understand structure-function relationships in cortical microcircuits is missing. Here,
we will assemble a team of researchers with complementary skills to tackle this problem. We will combine
approaches to study population coding and dynamics using two-photon calcium imaging during a novel and
complex decision task for mice, with measurements of connectivity in the imaged neurons using electron
microscopy (EM)-based connectomics. Furthermore, we will use our activity and connectivity data to develop a
data-driven model to explore structure-function relationships across cortical microcircuits.
We will apply our new approach to investigate how population codes, microcircuit connectivity, and structure-
function relationships differ across cortex to perform distinct computational tasks during perceptual decision-
making. Although it is well established that sensory and association cortices perform different functions, little is
known about the mechanisms underlying these different roles, including distinctions in microcircuit connectivity
and population coding schemes. In a first aim, we will compare population codes and microcircuit connectivity
for sensory stimuli and behavioral choices in visual cortex (V1; sensory cortex) and posterior parietal cortex
(PPC; association cortex). We will use computational tools to examine how distinct coding schemes provide
functional benefits. We will use EM connectomics in V1 and PPC for neurons imaged during a perceptual
decision task to probe structure-function relationships for stimulus and choice codes. We will develop a data-
driven recurrent neural network model to relate connectivity and population activity. In a second aim, we will
investigate how neuronal populations transform sensory information into behavioral choices using microcircuit
connectivity. We will develop a new statistical concept – intersection information – to identify activity patterns in
V1 and PPC that carry sensory information that informs behavioral choices. Using EM connectomics, we will
reconstruct the microcircuit connectivity between cells to test hypotheses about sensory-to-choice information
flow. Our work will be some of the first to compare population coding and microcircuit connectivity across cortical
regions and to explore structure-function relationships for perceptual decision-making.

## Key facts

- **NIH application ID:** 9995044
- **Project number:** 5R01NS108410-03
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Christopher D Harvey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,143,583
- **Award type:** 5
- **Project period:** 2018-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995044

## Citation

> US National Institutes of Health, RePORTER application 9995044, Studying perceptual decision-making across cortex by combining population imaging, connectomics, and computational modeling (5R01NS108410-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9995044. Licensed CC0.

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