Noise CorrelationsNCT06673303
Investigating The Role of Noise Correlations in Learning
A study testing Dynamic perceptual discrimination task and fMRI in people with Noise Correlations and Learning Quality, run by Brown University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Noise Correlations, Learning Quality
- What is being tested?
- Dynamic perceptual discrimination task and fMRI
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 47 participants
- Where
- 1 siteUnited States
- Study dates
- 14 Dec 2024to 1 Sep 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Age above 18
- Normal or correctable vision
Who cannot join
- Age under 18
- Claustrophobia
- Color blindness
- Neuroleptics medications
- History of drug abuse and/or alcoholism
- Conditions contraindicated for MRI such as:
Show all 20Show fewer
- 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:
- Stroke
- Traumatic brain injury
- Epilepsy
- Schizophrenia
- Manic depression with symptoms including but not limited to psychosis, mania, delusional thinking, and audio/visual hallucinations.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Cognitive and Molecular Challenges to Statistical Inference Across Healthy Aging
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.
Read the detailed description
Mammalian brains represent information using distributed population codes which provide a number of advantages from robustness to high representational capacity. However, for downstream readout neurons such codes pose formidable high-dimensional learning problems as a very large number of synaptic connections must be adjusted during learning in search of a suitable readout. Our recent theoretical work hypothesized that these high-dimensional learning problems can be simplified by inductive biases implemented through stimulus-independent noise correlations which express the degree to which a pair of neurons covary in their trial-to-trial fluctuations. While noise correlations have traditionally been viewed as providing constraints on representational capacity our recent work demonstrates that they simultaneously constrain readout learning. In some biologically relevant cases, they could theoretically speed learning by shaping the geometry of the underlying neural space to focus the gradient of learning onto task-relevant dimensions. However, this hypothesized role of noise correlations in shaping learning has not yet been empirically tested. Here the investigators elaborate an experimental framework to test the predicted role of noise correlations, as measured through covariation in fMRI multi-voxel BOLD activity patterns for a given stimulus, on learning in both familiar and novel contexts. In familiar contexts, useful noise correlations may be induced by top-down inputs from the prefrontal cortex that signal relevant task dimensions. Thus, the strength of noise correlations in task-relevant dimensions would predict faster learning about task-relevant features. On the other hand, in novel contexts when the relevant task dimensions are unknown, noise correlations may force gradients onto task-irrelevant dimensions and thus impair learning. Therefore, suppressing noise correlations, which might be achieved through neuromodulatory signaling, may speed learning by reducing bias early during learning or after a change in the task-relevant stimulus. Across our Aims, the investigators develop a plan to test the most basic predictions of our computational model using fMRI to characterize the geometry of noise correlations and pupillometry as a proxy for neuromodulatory signaling in human subjects. The planned research will provide the first empirical test of the role of noise correlations in learning.
- Conditions
- Noise Correlations, Learning Quality
- Being tested
- Dynamic perceptual discrimination task, fMRI
- Keywords
- noise correlations, perceptual learning, neural network, feature dimensions
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Dynamic perceptual discrimination taskBehavioural
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.
fMRIDiagnostic test
Participant brain imaging data will be collected concurrently while performing the perceptual discrimination task.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Behavioral: Dynamic perceptual discrimination task; Diagnostic Test: fMRI |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: Through completion of analysis, an average of 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Nov 2024
- Study start14 Dec 2024
- Primary completion1 Sep 2025
- Study completion1 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Noise CorrelationsStudies of the same condition, open and under way.
- More trials run by Brown UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 47 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brown University
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Responsible party
- Sponsor
- Investigators
- Matthew Nassar, PhD, Principal investigator, Brown University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06673303
- Sponsor's protocol ID
- 1806002126
- Other IDs
- 1P30GM149405-01
- Status last verified
- Nov 2024
- First submitted
- 18 Oct 2024
- Last updated
- 26 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: Anyone interested in IPD should reach out to the Principal Investigator
Publications
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.
PubMed 26851755Cohen 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.
PubMed 18940596Nassar 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.
PubMed 34193556
Study at a glance
From the registry record, last updated by the sponsor on 26 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Cognitive and Molecular Challenges to Statistical Inference Across Healthy Aging
- ClinicalTrials.gov ID
- NCT06673303
- Acronym / sponsor ID
- 1806002126
- Conditions
- Noise Correlations, Learning Quality
- Keywords
- noise correlations, perceptual learning, neural network, feature dimensions
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 47 participants (actual)
- Lead sponsor
- Brown UniversityUniversity, hospital or other
- Collaborators
- National Institute of General Medical Sciences (NIGMS)
- Study start
- 14 Dec 2024 (actual)
- Primary completion
- 1 Sep 2025 (actual)
- Study completion
- 1 Sep 2025 (actual)
- First posted
- 4 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Mammalian brains represent information using distributed population codes which provide a number of advantages from robustness to high representational capacity. However, for downstream readout neurons such codes pose formidable high-dimensional learning problems as a very large number of synaptic connections must be adjusted during learning in search of a suitable readout. Our recent theoretical work hypothesized that these high-dimensional learning problems can be simplified by inductive biases implemented through stimulus-independent noise correlations which express the degree to which a pair of neurons covary in their trial-to-trial fluctuations. While noise correlations have traditionally been viewed as providing constraints on representational capacity our recent work demonstrates that they simultaneously constrain readout learning. In some biologically relevant cases, they could theoretically speed learning by shaping the geometry of the underlying neural space to focus the gradient of learning onto task-relevant dimensions. However, this hypothesized role of noise correlations in shaping learning has not yet been empirically tested. Here the investigators elaborate an experimental framework to test the predicted role of noise correlations, as measured through covariation in fMRI multi-voxel BOLD activity patterns for a given stimulus, on learning in both familiar and novel contexts. In familiar contexts, useful noise correlations may be induced by top-down inputs from the prefrontal cortex that signal relevant task dimensions. Thus, the strength of noise correlations in task-relevant dimensions would predict faster learning about task-relevant features. On the other hand, in novel contexts when the relevant task dimensions are unknown, noise correlations may force gradients onto task-irrelevant dimensions and thus impair learning. Therefore, suppressing noise correlations, which might be achieved through neuromodulatory signaling, may speed learning by reducing bias early during learning or after a change in the task-relevant stimulus. Across our Aims, the investigators develop a plan to test the most basic predictions of our computational model using fMRI to characterize the geometry of noise correlations and pupillometry as a proxy for neuromodulatory signaling in human subjects. The planned research will provide the first empirical test of the role of noise correlations in learning.
Study design
- Primary purpose
- Basic science
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Dynamic perceptual discrimination task | Experimental | Behavioral: Dynamic perceptual discrimination task, Diagnostic Test: fMRIThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Age above 18
- Normal or correctable vision
Key exclusion criteria (20 listed)
- Age under 18
- Claustrophobia
- Color blindness
- Neuroleptics medications
- History of drug abuse and/or alcoholism
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Dynamic perceptual discrimination taskThe 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. | Behavioural | Dynamic perceptual discrimination task |
| fMRIParticipant brain imaging data will be collected concurrently while performing the perceptual discrimination task. | Diagnostic test | Dynamic perceptual discrimination task |
Primary outcome
Participant learning asymmetry in the behavioral task
Time frame: From the end of the behavioral session to the beginning of the scanning session, typically within a week
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.
Noise correlations in the brain
Time frame: Through completion of analysis, an average of 6 months
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 May 2026.
- Background: PubMed 26851755Fusi 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.
- Background: PubMed 18940596Cohen 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.
- Background: PubMed 34193556Nassar 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.
- Individual participant dataSharing planned: No. Anyone interested in IPD should reach out to the Principal Investigator
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Matthew Nassar, PhD principal investigator
Brown University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Dynamic perceptual discrimination task and fMRI in people with Noise Correlations and Learning Quality, run by Brown University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 20 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 26 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Participant learning asymmetry in the behavioral task (time frame: From the end of the behavioral session to the beginning of the scanning session, typically within a week). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06673303, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 May 2026.