# Neural Ensemble Modulation of Learned Visual Cues across Wake and Sleep States

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $38,276

## Abstract

Abstract:
Elucidating the neural circuitry involved in creating a learned association is crucial to understanding the biology
of both typical learning and maladaptive learning. Despite decades of research into learning and memory, the
mechanisms whereby newly learned associations are encoded in sensory cortices remain unclear. New
functional imaging methods in neuroscience research allow for the tracking of the same groups of neurons over
long time periods with single-neuron resolution, permitting us to ‘visualize’ how these memories are acquired,
consolidated, and forgotten in real-time. This research will probe the neural mechanisms whereby memories are
encoded in sensory association cortex with unparalleled specificity using two-photon calcium imaging,
optogenetics, and advanced behavioral tracking.
By utilizing visual cued fear conditioning and chronic in vivo two-photon calcium imaging in visual association
cortex (VisCtx) in transgenic GCaMP6-expressing mice, we have a powerful model to track hundreds of neurons
across learning processes in higher-order brain regions. Ultimately this enables us to track a neuronal ensemble
representing a memory as it forms. We hypothesize that a distinct ensemble of neurons in VisCtx that represents
a predicted shock-outcome will emerge during learning. In addition to local neuronal ensemble activity being
important for learning and memory, sleep and long-range circuit activity is also critical to encoding long-term
learned associations in the brain. Therefore, we will track neuronal ensembles representing a learned association
across vigilance states, to better understand the role for sleep behavior in forming a long-term memory trace.
We propose that an ensemble representing a learned association will be consolidated through reactivation during
sleep post-learning. Furthermore, we propose to incorporate imaging amygdala projections to cortex to integrate
circuit dynamics into our understanding of long-term storage of memory in cortex. We hypothesize that
establishing a predicted-outcome responsive neuronal ensemble will be dependent on amygdala feedback to
cortex after learning. Our approach allows for novel investigation of the naturalistic emergence of a learned
association, including elucidating some of the circuits and behaviors that promote encoding of long-term memory
in cortical circuits.

## Key facts

- **NIH application ID:** 10173644
- **Project number:** 5F31MH123132-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Alexa Faulkner
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $38,276
- **Award type:** 5
- **Project period:** 2020-05-04 → 2022-11-03

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173644

## Citation

> US National Institutes of Health, RePORTER application 10173644, Neural Ensemble Modulation of Learned Visual Cues across Wake and Sleep States (5F31MH123132-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10173644. Licensed CC0.

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