# Multiplexing working memory and timing: Encoding retrospective and prospective information in transient neural trajectories.

> **NIH NIH RF1** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $1,841,418

## Abstract

Abstract
A general principle of brain function is the ability to store information about the past to better predict and prepare for the
future. Working memory and timing are two computational features that evolved to allow the brain to use recent
information about the past to accomplish short-term goals. Working memory refers to the ability to transiently store
information in a flexible manner, while timing refers to the ability to generate well timed motor responses, modulate
attention in time, and predict when external events will occur. To date working memory and timing have primarily been
treated as independent processes. Here we propose that because the brain seeks to use information about the past to
predict the future, that working memory and timing are often multiplexed. Specifically, that the neural patterns of activity
recorded during the delay period of many working memory tasks encodes both retrospective information about the
past, as well as prospective predictions about the future. To test this hypothesis, we have developed novel variant of
the delayed- nonmatch-to-sample task, in which the first cue predicts the duration of the delay, that is, how long an item
must be held in working memory. This task will allow us to determine if network level population responses encode both
retrospective information about the past and prospective information about delay duration. Preliminary results from a
supervised recurrent neural network model predict that the temporal structure of the neural patterns of activity elicited
by both cues will be different. This prediction will be tested using large scale Ca2+-imaging to characterize the
spatiotemporal patterns of activity in brain areas associated with working memory and timing. Additionally, optogenetic
perturbation experiments and longitudinal characterization of the emergence of neural patterns of activity will be
performed. These experiments, will in turn, be used to ground computational studies aimed at understanding the
neuronal and circuit level learning rules that underlie the emergence of patterns that encode working memory and time.

## Key facts

- **NIH application ID:** 9971022
- **Project number:** 1RF1NS116589-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** DEAN V BUONOMANO
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,841,418
- **Award type:** 1
- **Project period:** 2020-04-15 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9971022

## Citation

> US National Institutes of Health, RePORTER application 9971022, Multiplexing working memory and timing: Encoding retrospective and prospective information in transient neural trajectories. (1RF1NS116589-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9971022. Licensed CC0.

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