# A latent variable model for quantifying social behavior in rodents

> **NIH NIH F31** · PRINCETON UNIVERSITY · 2022 · $30,752

## Abstract

PROJECT SUMMARY
Computational methods for quantifying mammalian natural behavior, including social interactions, are crucial for
developing a sophisticated understanding of the neural basis of behavior. Yet a full description of behavior
consists of much more than an animal’s actions. External cues (such as the actions of a social partner) drive our
behavioral responses, and our responses to those cues can depend on context, our internal mental state, and
prior experience. We may approach an individual when we feel safe, or attack that same individual when we feel
threatened. The resulting complexity makes natural behavior — and social interactions in particular —
challenging to study. To overcome this barrier, I propose to develop broadly applicable models to predict natural
and social behavioral dynamics in mice based on changing external cues and internal states. These models will
use unsupervised learning techniques to quantify and predict complex patterns of behavior in an interpretable
manner while linking social behaviors to changes in neural activity across multiple timescales. Together, these
models will provide an unprecedented view of how different neural populations encode the internal states that
shape social behaviors as they unfold over time. The first aim is to fit a set of increasingly complex datasets with
flexible latent-state models that describe how natural and social behaviors arise in response to factors such as
external cues and time-varying internal states. In the second aim, I will apply this modeling framework to calcium
recordings in dopaminergic projections to the Nucleus Accumbens and Tail of the Striatum as well as
glutamatergic cell bodies in the Lateral Habenula — all neural populations shown to respond in social contexts.
I will determine how these neural populations differentially encode sensory inputs, internal states, and behavioral
outputs. I will also examine how the activity in each neural population correlates with transitions between different
behaviors and internal states and how these representations change with experience. The proposed work will
break new ground by applying novel computational tools and sophisticated, unsupervised behavioral
quantification methods to discover the internal and external variables that shape natural behaviors as well as the
underlying neural correlates of social interactions. Together, the new computational modeling techniques that I
am proposing will advance several goals of the NIMH Theoretical and Computational Neuroscience Program:
they (1) contain distinct levels of analysis, (2) link neuronal and behavioral processes, (3) enhance predictions
of high-resolution behavioral data along with neural units of analysis, and (4) provide effective explanatory
techniques and methods of interpretation for their results.

## Key facts

- **NIH application ID:** 10535865
- **Project number:** 1F31MH131304-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Iris Stone
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $30,752
- **Award type:** 1
- **Project period:** 2022-09-17 → 2025-09-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10535865

## Citation

> US National Institutes of Health, RePORTER application 10535865, A latent variable model for quantifying social behavior in rodents (1F31MH131304-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10535865. Licensed CC0.

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