# Computational, Neural, and Behavioral Studies of Competition-Dependent Learning

> **NIH NIH R01** · PRINCETON UNIVERSITY · 2020 · $31,707

## Abstract

PROJECT SUMMARY
Our overarching goal is to understanding how stored memories change as a function of experience. The pro-
posed work builds on prior research showing a U-shaped relationship between memory activation and learn-
ing, whereby strong activation leads to synaptic strengthening, moderate activation leads to synaptic weaken-
ing, and no activation leads to no change in synaptic strength. The present grant focuses on the implications of
this U-shaped relationship for representational change: Learning is not just about making memories stronger or
weaker—it can also decrease neural overlap between memories (differentiation) or increase neural overlap
(integration). These neural changes can have profound effects on memory retrieval: Decreased overlap can
reduce interference, at the cost of preventing generalization. Our specific goal is to construct and test a com-
putational model of representational change and how it is shaped by competitive neural dynamics. When im-
plemented in neural networks that are capable of self-organizing internal representations, our theory makes
clear, novel predictions about when differentiation and integration will occur: Differentiation of memories A and
B will occur when (i) B is moderately activated while processing A, causing weakening of connections between
B and A, and (ii) B is reactivated later, allowing it to acquire new features that do not overlap with A; by con-
trast, integration will occur if B is strongly activated during A, causing strengthening of connections between B
and A. Aim 1 will use neural network simulations to address vexing puzzles in the literature and to generate
novel empirical predictions. Aim 2 will test these predictions using behavioral and fMRI experiments focused
on learning of new associations in the hippocampus, with a particular emphasis on testing the model's predic-
tions about how competitive dynamics relate to representational change. Aim 3 will test the model's predictions
regarding cortical plasticity, using a novel sketching task that induces competition between representations of
familiar objects. Representational change will be assessed behaviorally in terms of how sketches and object
recognition change over learning and neurally using fMRI of visual cortex; a deep neural network model of
the ventral stream will be used to measure changes in the features of sketches. In summary: The proposed
studies use multiple innovative approaches (fMRI pattern analysis, neural network modeling, free-form object
sketching, and computer vision) to address the fundamental question of when experience causes neural repre-
sentations to differentiate or integrate, thereby advancing our basic understanding of neuroplasticity. Improving
our understanding of neural differentiation could have transformative implications for treating cognitive deficits
in a wide range of clinical conditions, including stroke, dyslexia, and dementia. In all of these conditions, cogni-
tive deficits can...

## Key facts

- **NIH application ID:** 10187834
- **Project number:** 3R01MH069456-14S1
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** KENNETH A NORMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $31,707
- **Award type:** 3
- **Project period:** 2004-02-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10187834

## Citation

> US National Institutes of Health, RePORTER application 10187834, Computational, Neural, and Behavioral Studies of Competition-Dependent Learning (3R01MH069456-14S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10187834. Licensed CC0.

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