# CRCNS: Neural computations underlying sequence memory consolidation in sleep

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $352,432

## Abstract

The ability to store and retrieve sequentially related information is arguably the foundation of intelligent
behavior. It allows us to predict the outcomes of sensory situations, to achieve goals by generating
sequences of motor actions, to 'mentally' explore the possible outcomes of different navigational or motor
choices, and ultimately to communicate through complex verbal sequences generated by flexibly chaining
simpler elemental sequences learned in childhood. Sleep extracts invariant features from the learned
information, leading to the generation of explicit knowledge and insight. Despite remarkable progress,
including work by PI and co-PI of this project, many critical questions remain about role of sleep in memory
and learning. Here we propose to address these questions through the development of computational
models that are probed and validated through in vivo experiments in mice. We will explore the hippocampal
(HC) and neocortical (NC) mechanisms underlying how sequences are acquired and subsequently
consolidated through off-line replay during Slow Wave Sleep (SWS) in a manner that minimizes
interference between overlapping and/or reversed sequences and how NC may chain sequence fragments
together. We combine computer modelling (Bazhenov) of spiking neural networks that mimic awake and
SWS brain dynamics, including NC slow oscillations and HC Sharp Wave Ripples (SWR), with high density
neural ensemble recordings (McNaughton) in mice, in a controlled behavioral setting including sequence
learning and subsequent, chemogenetically induced SWS, which makes it possible to observe how learned
sequence representations in NC evolve spontaneously over prolonged periods of SWS. The PIs have been
collaborating on and discussing this topic for the past several years, resulting in specific hypotheses that
can be explored in real brains. The project outcome will provide a better understanding of how knowledge
is extracted from experience, what brain circuits are involved and how brain dynamics are shaped by the
development of a rich internal model of the world, including the ability to predict the outcomes of current
situations and one's own actions in that context.
RELEVANCE (See instructions):
The ability to store and retrieve sequentially related information is the foundation of intelligent behavior and
brain executive function. Deficits in this ability, resulting from disruption of brain circuits, are seen in
depression, schizophrenia and PTSD. Better understanding of the mechanisms and brain dynamics
underlying the acquisition, consolidation and retrieval of sequential information will lead to interventions to
improve cognitive performance, memory and learning in healthy subjects and patients with mental illness.

## Key facts

- **NIH application ID:** 10646435
- **Project number:** 5R01MH125557-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** MAKSIM V BAZHENOV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $352,432
- **Award type:** 5
- **Project period:** 2020-08-10 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10646435

## Citation

> US National Institutes of Health, RePORTER application 10646435, CRCNS: Neural computations underlying sequence memory consolidation in sleep (5R01MH125557-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10646435. Licensed CC0.

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