Investigating The Role of Noise Correlations in Learning

Summary

A fundamental problem in neuroscience is how the brain computes with noisy neurons. An advantage of population codes is that downstream neurons can pool across multiple neurons to reduce the impact of noise. However, this benefit depends on the noise associated with each neuron being independent. Noise correlations refer to the covariance of noise between pairs of neurons, and such correlations can limit the advantages gained from pooling across large neural populations. Indeed, a large body of theoretical work argues that positive noise correlations between similarly tuned neurons reduce the representational capacity of neural populations and are thus detrimental to neural computation. Despite this apparent disadvantage, such noise correlations are observed across many different brain regions, persist even in well-trained subjects, and are dynamically altered in complex tasks. The investigators have advanced the hypothesis that noise correlations may be a neural mechanism for reducing the dimensionality of learning problems. The viability of this hypothesis has been demonstrated in neural network simulations where noise correlations, when embedded in populations with fixed signal-to-noise ratio, enhance the speed and robustness of learning. Here the investigators aim to empirically test this hypothesis, using a combination of computational modeling, fMRI and pupillometry. Establishing a link between noise correlations and learning would open the door to an investigation into how brains navigate a tradeoff between representational capacity and the speed of learning.

Conditions

• Noise Correlations
• Learning Quality

Interventions

BEHAVIORAL

Dynamic perceptual discrimination task

The study featured two task conditions, each of which required the integration of information from both stimulus dimensions. In each condition, participants viewed a stimulus containing motion and color information and were required to specify one of two possible responses. Within each condition, rules and the response mapping changed occasionally, but always by changing on a fixed feature dimension (ie. rightward/purple, leftward/orange). These uncued intra-dimensional shifts involved translational shifts in the learning boundary, requiring them to adapt their decision making within a familiar dimension. These shifts compelled participants to continuously adjust their learning strategies by focusing on the most relevant feature dimension.

DIAGNOSTIC_TEST

fMRI

Participant brain imaging data will be collected concurrently while performing the perceptual discrimination task.

Sponsors & Collaborators

• National Institute of General Medical Sciences (NIGMS)

  collaborator NIH

• Brown University

  lead OTHER

Principal Investigators

• Matthew Nassar, PhD · Brown University

Study Design

Allocation

NA

Purpose

BASIC_SCIENCE

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-12-14

Primary Completion

2025-09-01

Completion

2025-09-01

FDA Device

Yes

Countries

• United States

Study Locations