Friday, October 7
10:00 AM - 4:00 PM EDT
Skylight Room and via Zoom
watch the lectures online
The physics of behavior aims at a more complete characterization of complex animal movements under more naturalistic conditions. This symposium samples recent progress in this field. We will hear from theorists and experimentalists as they discuss how ideas from dynamical systems theory and statistical physics can create simple yet predictive models of animal behavior across scales, and how new methods, driven by advances in computer vision and deep learning, are bringing us toward precise and automated quantifications of behavior in diverse organisms and contexts.
10:00 AM
Quantifying behavior using deep learning
Talmo Pereira
Salk Institute
—
11:30 AM
Break
—
12:00 PM
Physics of behavior across scales:
from partial observations to long time scales through maximally predictive states
António Carlos Costa
École Normale Supérieure, Paris
—
1:30 PM
Lunch in Skylight Room
—
2:30 PM
Quantifying the structure of multi-agent behavior
Ann Kennedy
Northwestern University
—
-
A core goal of neuroscience is to understand how the brain adaptively orchestrates movements to execute complex behaviors. Quantifying behavioral dynamics, however, has historically been prohibitively laborious or technically intractable, particularly for unconstrained and naturalistic behaviors which the brain evolved to produce. Driven by advances in computer vision and deep learning, new methods are being developed to overcome these limitations and enable precise and automated quantification of behavior from conventional across species and experimental settings. In this talk we will: introduce the problem of pose tracking for behavioral quantification; show how deep learning can be employed to achieve markerless motion capture; and highlight examples of how our work on making this technology accessible through open-source tools like SLEAP (sleap.ai) is enabling studies across domains and application areas ranging from social and motor neuroscience in flies, rodents, and primates, to ecology, digital humanities, and even plant biology to tackle climate change.
-
Recent years have seen an explosion in our ability to measure the movement dynamics of animals in naturalistic environments. However, such data poses tremendous challenges for quantitative analysis, as behavior is generally partially-observed, unpredictable, multiscale and far from equilibrium. We here show how we can combine ideas from dynamical systems theory and statistical physics to draw simple yet predictive models of animal behavior across scales. We construct maximally-predictive states by concatenating measurements in time, partitioning the resulting sequences using maximum entropy, and choosing the sequence length to maximize short-time predictive information. We use transitions between these states to analyze reconstructed dynamics through transfer operators, revealing timescale separation with long-lived collective modes through the operator spectrum. Applied to the behavior of the nematode worm C. elegans, we bridge sub-second posture fluctuations and long range effective diffusion in foraging behavior, recovering the ``ballistic-to-diffusive'' transition in the worm's centroid trajectories.
-
Animals engage in a variety of complex dyadic and group interactions, including predator-prey behavior, reproduction, parenting, and conspecific-directed territorial aggression. A central function of the nervous system is to organize the moment-to-moment structure of animals’ actions in a way that serves an animal’s fitness and reproductive success. In this talk, I will discuss quantitative approaches that our group has taken to characterize the actions of laboratory mice during naturalistic social interactions. I’ll then talk about how these descriptive models relate to normative models of behavioral control, and the challenges inherent in relating these two levels of thinking about animal behavior.
This event is sponsored in part by the Center for the Physics of Biological Function, a joint effort of the CUNY Graduate Center and Princeton University.