# Neural Representations of Abstract Sequences

> **NIH NIH F32** · BROWN UNIVERSITY · 2022 · $69,802

## Abstract

PROJECT SUMMARY
Research: The proposed research will investigate the neural representation of abstract sequences. Abstract
sequences are defined by higher-order patterns that generalize across sensory stimuli (e.g., AAAB, &&&*).
Deficits in sequence processing arise in a range of neuropsychiatric disorders, including Obsessive-Compulsive
Disorder (OCD), Parkinson’s disease, and frontal lobe dysfunction. The source of these deficits is difficult to
determine, as the neural mechanisms behind them remain poorly understood. Previous work suggests that
lateral prefrontal cortex (LPFC) contributes to sequence processing, but the neural representation of abstract
sequences has not been investigated. The proposed studies address this knowledge gap. I focus on two
questions: whether sequence representations are abstract, and whether sequential behavior requires multiple
neural subpopulations to facilitate flexible coding. I approach these questions using two complementary
methods: neural recording in nonhuman primates and modeling with recurrent neural networks (RNNs). In Aim
1, I will use fMRI-guided neural recording to test the hypothesis that sequence representation in macaque LPFC
is independent of stimulus identity, leading to generalizability across stimuli and task contexts. In Aim 2, I will
use low-rank RNNs to test whether sequence monitoring requires more neuronal subpopulations than a non-
sequential delayed-match to sample task, a signature of flexible stimulus-response mapping.
These studies will expand our knowledge of abstract sequence representation. Moreover, the results serve as a
case study to understand two key features of generalization: representational stability and implementational
flexibility. In combination with other work from our group, the data from this study will create a bridge between
primate electrophysiology, primate fMRI, and human fMRI, informing the development of cross-species models
of human disease.
Environment & Training: My environment is ideally suited for the proposed training. Drawing on their expertise,
my Sponsor and Co-Sponsor will train me in multi-electrode recording, analysis of large neural datasets, and
computational modeling using RNNs. Furthermore, my Sponsor leads one of the few labs conducting fMRI
experiments in awake monkeys, giving us the unique ability to functionally target regions of interest for neural
recording. The collaborative research community, research facilities, and computational resources at Brown
further support my proposed training. In addition to my research, my training will include workshops in neural
analysis and modeling, participation in scientific meetings, professional development, and training in scientific
communication, mentorship, and responsible conduct in research. The proposed training will provide ideal
preparation for my career goal of combining computational and systems neuroscience to study generalization in
my own lab.

## Key facts

- **NIH application ID:** 10464331
- **Project number:** 1F32MH127878-01A1
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Katherine E Conen
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $69,802
- **Award type:** 1
- **Project period:** 2022-05-04 → 2025-05-03

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10464331

## Citation

> US National Institutes of Health, RePORTER application 10464331, Neural Representations of Abstract Sequences (1F32MH127878-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10464331. Licensed CC0.

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