# Mechanisms of efficient coding of dynamic visual motion signals for pursuit

> **NIH NIH R01** · DUKE UNIVERSITY · 2021 · $376,270

## Abstract

Abstract
Current theories of how the brain estimates stimuli from sensory populations are based on non-adapting
responses to single-parameter, constant stimuli over long time windows – highly unnatural conditions. To
understand sensory circuits we need to determine how dynamic, multi-dimensional stimuli are decoded for rapid
behaviors by adapting neurons. Progress may depend less on our ability to record ever-larger samples of cortical
activity, and more on our ability to relate the activity we observe to the brain's read out. In short, we need a proxy
for the answer that we are trying to model from our recordings – a precise behavioral response. We propose to
exploit the close connection between cortical visual motion representation and pursuit eye movements in
monkeys to study two significant problems: (1) how cortico-pontine projections transform the distributed place
code for visual signals in cortex to a form better suited to driving motor areas and (2) the how the brain forms
stable sensory estimates with adapting neurons. Our focus is how activity in area MT is transformed by
downstream projections to the dorsolateral pontine nucleus (DLPN). DLPN transmits estimates of retinal motion
to the flocculus to initiate and maintain pursuit, although other pathways also contribute. The pursuit system is
an excellent model for studying sensory decoding because little noise is added in downstream motor processing-
- the eye movement is a faithful rendering of the brain's estimate of target motion. Although the eye pursues
correctly, we showed that MT neurons do not maintain a fixed relationship between firing rate and retinal motion.
Our first aim is to determine if the MT-DLPN projection reduces noise, filters out talk irrelevant signals, and alters
the coordinate from direction-speed to the H-V axes of the extra-ocular muscles. We propose to record from MT
and DLPN in behaving monkeys, using tetrodes to record groups of nearby neurons and eye coils to monitor eye
movements very accurately. Our second aim is to determine how the brain recovers veridical stimulus estimates
from an adapting sensory population. Adaptation is ubiquitous in the brain, often driven by rapid changes in
natural stimuli. In the previous grant period, we showed that MT neurons adapt their gain to the direction variance
of a dynamic motion stimulus, making good use of a limited response bandwidth. Gain adaptation increases bit
rates but it also creates ambiguity because the mapping between motion direction and firing rate is not fixed. In
our second aim, we will investigate whether a downstream area needs information about the stimulus variance
to properly estimate motion direction. Pursuit behavior shows that the brain solves this problem, but gain
adaptation seems to foil our current decoding models. We will use information-based methods applied to MT
and DLPN data to determine how to form a successful read-out. Our proposed work will create more realistic
theories of senso...

## Key facts

- **NIH application ID:** 10121463
- **Project number:** 2R01EY023371-07A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Leslie Carol Osborne
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $376,270
- **Award type:** 2
- **Project period:** 2014-02-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10121463

## Citation

> US National Institutes of Health, RePORTER application 10121463, Mechanisms of efficient coding of dynamic visual motion signals for pursuit (2R01EY023371-07A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10121463. Licensed CC0.

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