# Topological bridges between circuits, models, and behavior

> **NIH NIH RF1** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $2,814,033

## Abstract

Project summary
The plight of the neuroscientist trying to understand the brain using linear analysis methods is akin to studying
the makeup of the ocean using the bits you find with a metal detector. Everything we know about the neural
basis of decision making, from biology to computation to behavior, makes it clear that the relationship between
neurons and behavior is profoundly nonlinear. However, for good mathematical reasons, our attempts to
understand that relationship typically rely only on linear measures. These measures have an especially hard
time dealing with the reality that neural networks are far from static. Indeed, the flexibility of interactions between
neurons, while adding an additional nonlinearity, is a critically important clue about underlying mechanisms and
computations. The goal of the proposed project is to test the hypothesis that nonlinear measures of correlated
variability in a population of neurons will 1) establish a strong link between neurons and perceptual decisions, 2)
constrain models of the circuit mechanisms by which cognition affects perception, and 3) predict the effects of
causally manipulating different subtypes of inhibitory interneurons on population activity. We will use and
develop methods from algebraic topology to characterize the activity of neuronal populations in a holistic,
nonlinear way. Our project leverages the complementary strengths between three highly interactive approaches:
primate neurophysiology and psychophysics, modeling neuronal circuits, and two photon imaging and
optogenetic manipulation of subtypes of inhibitory interneurons in mice. In Aim 1, we will test the hypothesis that
sensory and cognitive processes including contrast, adaptation, attention, and motivation affect performance on
visual tasks exactly when they change the topological signatures of the correlated variability in visual or parietal
cortex. In Aim 2, we will use a biophysically realistic model to understand which changes in a cortical circuit
would or would not change the topological signatures of neuronal population activity. In Aim 3, we will test the
predictions of our model to understand how manipulating the activity of different subtypes of inhibitory
interneurons affects topological summaries of neuronal activity and information processing in the network. This
work uses novel mathematical ideas to bridge different levels of the study of cortical circuits. It will have
implications for our understanding of the relationship between neuronal circuits and behavior across species,
systems, and theoretical approaches.

## Key facts

- **NIH application ID:** 10208403
- **Project number:** 1RF1NS121913-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Marlene Rochelle Cohen
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,814,033
- **Award type:** 1
- **Project period:** 2021-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10208403

## Citation

> US National Institutes of Health, RePORTER application 10208403, Topological bridges between circuits, models, and behavior (1RF1NS121913-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10208403. Licensed CC0.

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