Investigating The Role of Noise Correlations in Learning

NCT06673303 | Completed FDA Device

• 2 other identifiers: 18060021261P30GM149405-01
• Study Type: interventional
• Target: 47
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

noise correlations; perceptual learning; neural network; feature dimensions

Outcome Measures

Primary Outcomes (2)

• Participant learning asymmetry in the behavioral task

  The participants' learning asymmetries on the task-relevant and task-irrelevant dimensions are evaluated with our reinforcement learning model that recover their learning gradient on the respective learning dimensions.

  From the end of the behavioral session to the beginning of the scanning session, typically within a week

• Noise correlations in the brain

  The investigators will identify the noise correlation - the ratio of trial-by-trial variability associated with a stimulus along the task-relevant versus task-irrelevant coding axes. The coding axes will be decoded from region of interests using multi-voxel patterns associate with individual trials.

  Through completion of analysis, an average of 6 months

Study Arms (1)

Dynamic perceptual discrimination task

EXPERIMENTAL

The task 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.

Behavioral: Dynamic perceptual discrimination task; Diagnostic Test: fMRI

Interventions

Dynamic perceptual discrimination task BEHAVIORAL

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.

Dynamic perceptual discrimination task

fMRI DIAGNOSTIC_TEST

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

Dynamic perceptual discrimination task

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age above 18
• Normal or correctable vision

You may not qualify if:

• Age under 18
• Claustrophobia
• Color blindness
• Neuroleptics medications
• History of drug abuse and/or alcoholism
• Conditions contraindicated for MRI such as:
• Surgical implant that is not MRI compatible
• Metal fragments in the body
• Tattoo with metallic ink
• Eye diseases / impairment:
• Cataracts
• Macular degeneration
• Retinopathies
• Partial vision loss
• Medical history:

Sponsors & Collaborators

• Brown University: lead
• National Institute of General Medical Sciences (NIGMS): collaborator

Study Sites (1)

Brown University

Providence, Rhode Island, 02906, United States

Location

Related Publications (3)

• Fusi S, Miller EK, Rigotti M. Why neurons mix: high dimensionality for higher cognition. Curr Opin Neurobiol. 2016 Apr;37:66-74. doi: 10.1016/j.conb.2016.01.010. Epub 2016 Feb 4.

  PMID: 26851755 BACKGROUND

• Cohen MR, Newsome WT. Context-dependent changes in functional circuitry in visual area MT. Neuron. 2008 Oct 9;60(1):162-73. doi: 10.1016/j.neuron.2008.08.007.

  PMID: 18940596 BACKGROUND

• Nassar MR, Scott D, Bhandari A. Noise Correlations for Faster and More Robust Learning. J Neurosci. 2021 Aug 4;41(31):6740-6752. doi: 10.1523/JNEUROSCI.3045-20.2021. Epub 2021 Jun 30.

  PMID: 34193556 BACKGROUND

MeSH Terms

Interventions

Magnetic Resonance Imaging

Intervention Hierarchy (Ancestors)

Tomography; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Matthew Nassar, PhD

  Brown University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: BASIC SCIENCE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 18, 2024
• First Posted: November 4, 2024
• Study Start: December 14, 2024
• Primary Completion: September 1, 2025
• Study Completion: September 1, 2025
• Last Updated: May 26, 2026

Record last verified: 2024-11

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)