# Adaptive population codes for flexible visually-guided behaviors

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $383,150

## Abstract

Summary/Abstract
 Active vision requires encoding and remembering relevant information based on current task goals.
Classic accounts posit that sensory encoding, attentional selection and working memory are mediated by
persistent changes in the firing rates, or the gain, of visually responsive neurons that have a fixed tuning profile
(termed “pure” or “fixed-selectivity” neurons). The focus on gain modulations in fixed-selectivity neurons has
revealed a great deal about the basic mechanisms of attention and memory. However, it is becoming increasingly
clear that dynamic changes in task demands may require more flexible coding schemes. For example, holding
information in working memory in the same format as the stimulus-evoked response may lead to interference
with new sensory inputs. Similarly, flexibly encoding sensory representations to complete one task – say a simple
choice between two motor responses – might require a reconfiguration of the representation if another stimulus-
response mapping suddenly becomes relevant. Finally, sensory codes must be flexible in the sense that early
in processing they should form high-dimensional representations to represent as much information as possible
about the current state of the world. Later in processing, when a decision or motor response needs to be made,
the code should collapse to only represent the smaller subset of relevant choices. All of these computations are
more naturally accomplished via the operation of neurons that have flexible tuning for both sensory features and
for task demands (termed “mixed-selectivity” neurons).
 Based on these considerations, we hypothesize that flexible behaviors are supported by mixed-selectivity
neurons that “rotate” high-dimensional neural codes to become robust to interference or to sub-serve other
changes in task demands. We will use modelling, psychophysics, and functional magnetic resonance imaging
(fMRI) to test predictions about how mixed-selectivity should modulate large-scale activation patterns that are
measured non-invasively in human subjects. Collectively, this work will challenge traditional theories of sensory
encoding, attention, and working memory that are based on the notion of fixed-selectivity, and will provide
important constraints on models of visual information processing to support more targeted diagnoses and
interventions in clinical settings.

## Key facts

- **NIH application ID:** 10320050
- **Project number:** 5R01EY025872-12
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** John T Serences
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $383,150
- **Award type:** 5
- **Project period:** 2021-01-01 → 2024-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10320050

## Citation

> US National Institutes of Health, RePORTER application 10320050, Adaptive population codes for flexible visually-guided behaviors (5R01EY025872-12). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10320050. Licensed CC0.

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