# Cortical adaptation in neural populations

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $227,288

## Abstract

Project Summary / Abstract
We do not fully understand how the cortex adapts its limited resources to optimally represent sensory information
in time-varying environments. The proposed studies aim to mathematically characterize adaptation in
populations of cortical neurons by applying novel geometric analyses and developing a normative theory that
accounts for the experimental data.
In Aim 1, we will test two hypothetical properties of adaptation. Direction invariance posits that the direction of
the population response to a given stimulus is approximately invariant to changes in its prior probability of being
observed. A power law relationship postulates that the ratio of the magnitudes of population response to a fixed
stimulus across two statistical environments is a power of the ratio between its prior probabilities. An empirical
confirmation of these findings will deliver the first mathematical characterization of adaptation capable of
predicting population responses in a new environment from the population responses in a known one.
In Aim 2, we will develop a normative theory for the power law property. We will consider the hypothesis that a
power law emerges as a tradeoff between discrimination performance and the metabolic cost of cortical
representation. We will tackle this question using two synergistic approaches: (a) the analysis of simple,
analytically tractable models of adaptation and (b) a larger class of auto-encoder models, which provide a flexible
way to study the problem numerically.
The proposed studies are significant because they fill a major gap in the field by taking a rigorous look at cortical
adaptation at the population level, gathering critical data not yet available to the community, and analyzing them
using innovative geometric analyses and theoretical modeling. These rigorous studies rely on solid concepts
from representational geometry and efficient coding. Solid, preliminary findings show the work has a good
chance of transforming our conceptual view of adaptation at the level of neural populations, generating ideas
readily applicable to other sensory modalities and systems.

## Key facts

- **NIH application ID:** 10808374
- **Project number:** 1R21EY035064-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** DARIO L RINGACH
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $227,288
- **Award type:** 1
- **Project period:** 2024-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10808374

## Citation

> US National Institutes of Health, RePORTER application 10808374, Cortical adaptation in neural populations (1R21EY035064-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10808374. Licensed CC0.

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