# Probabilistic coding in cortical populations

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2021 · $375,480

## Abstract

PROJECT SUMMARY
One of the most impressive feats the brain performs is its use of information that is rife with uncertainty to
successfully guide behavior. That the world is full of ambiguous stimuli and perceived through noisy sensors is
clear. However, whether and how the brain makes use of this uncertainty is an open and important question.
Behavioral studies using tasks with noisy or ambiguous stimuli suggest that subjects' performance is nearly
optimal. These results strongly suggest that the brain must represent and use information about sensory
uncertainty itself in addition to information about parameters of the stimulus, yet many theories about neural
coding do not account for how such uncertainty could be represented or used. The theory of Probabilistic
Population Coding (PPC) does provide such an account, claiming that populations of neurons encode
estimates of stimulus parameters and uncertainty regarding those estimates simultaneously in the form of a
“likelihood function” over the stimulus. PPCs further provide neurally plausible mechanisms for performing a
number of Bayesian computations, however, this promising theory has never been tested neurophysiologically
at the population level.
 To study the mechanisms by which the brain represents and uses sensory uncertainty to guide adaptive
behavior, the current proposal will combine multi-electrode recordings, computational neuroscience and
psychophysics. Specifically, we will study visual decision-making in an orientation classification task, which we
have previously shown that primates perform near optimally and which requires the use of uncertainty
information to achieve optimal performance. In Aim 1, we will test the hypothesis that populations of neurons
jointly encode likelihood functions as predicted by PPC, by recording from V1 while subjects perform the
classification task. In Aim 2, we will test whether a shared encoding of uncertainty information between V1 and
prefrontal cortex (PFC) leads to a functional correlation between these two areas. Through our combination of
in vivo population recordings in awake, behaving primates, the proposed project is strongly positioned to test
the core hypothesis behind PPC and elucidate the mechanisms by which the brain makes possible optimal
behavioral performance in a noisy environment.

## Key facts

- **NIH application ID:** 10133077
- **Project number:** 5R01EY026927-05
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Wei Ji Ma
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $375,480
- **Award type:** 5
- **Project period:** 2017-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10133077

## Citation

> US National Institutes of Health, RePORTER application 10133077, Probabilistic coding in cortical populations (5R01EY026927-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10133077. Licensed CC0.

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