# Neural mechanisms for reducing interference during episodic memory formation

> **NIH NIH R01** · UNIVERSITY OF OREGON · 2024 · $52,274

## Abstract

Project Summary/Abstract
One of the biggest challenges to successful remembering is the potential for interference between similar
memories. For every password, name, or parking space that we store in memory, there are many other
passwords, names or parking spaces that we have already learned or will learn in the future. While
interference is a factor in relatively benign examples of ‘normal’ forgetting, it is also a major factor in
clinically-significant examples of forgetting that occur with aging or dementia. Thus, there is a fundamental
need to understand the neural mechanisms that support the acquisition of similar memories while
minimizing interference and corresponding forgetting. Computational models of episodic memory have
emphasized the critical role of pattern separation in reducing memory interference. Pattern separation
involves coding similar memories such that differences between memories are exaggerated and the
potential for confusion is thereby minimized. While there is agreement that pattern separation is
implemented by the hippocampus—and that it is important for reducing memory interference—there remain
several fundamental gaps in our understanding of how, when, and why pattern separation occurs. In
particular, there remains ambiguity as far as (a) the learning contexts in which each mechanism might be
recruited, (b) what the corresponding neural signatures of each mechanism are, and (c) the specific
behavioral consequences associated with the engagement of each mechanism. We will conduct a
systematic investigation of the contexts in which pattern separation occurs, leveraging sophisticated
neuroimaging (fMRI) techniques to characterize patterns of neural activity and to link these patterns of
activity to behavioral expressions of memory. The research represents a strong synthesis of psychology
and neuroscience questions with an emphasis on learning mechanisms inspired by computational models
and analysis approaches that draw from the fields of machine learning and data mining.
In this diversity supplement, we request funding to support Mr. Julian Gamez who recently graduated (B.S.
degree) from the University of Oregon and is preparing to apply to Ph.D. programs. Mr. Gamez will
specifically focus on establishing how memory interference influences the dimensionality of neural
representations of memories and corresponding behavioral expressions. This novel approach will address
competing theoretical accounts and will strongly complement the overall goals of the parent award. It will
also provide an outstanding opportunity for Mr. Gamez to gain expertise learning sophisticated
computational methods and applying these methods to human fMRI and behavioral data.

## Key facts

- **NIH application ID:** 10987721
- **Project number:** 3R01NS089729-09S1
- **Recipient organization:** UNIVERSITY OF OREGON
- **Principal Investigator:** BRICE Alan KUHL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $52,274
- **Award type:** 3
- **Project period:** 2014-09-30 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10987721

## Citation

> US National Institutes of Health, RePORTER application 10987721, Neural mechanisms for reducing interference during episodic memory formation (3R01NS089729-09S1). Retrieved via AI Analytics 2026-06-03 from https://api.ai-analytics.org/grant/nih/10987721. Licensed CC0.

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