Quantifying Naturalistic Animal Behavior

Course Aim

This course is aimed at students looking at animals and wondering how to capture and describe their behavior in the best way.

Course Description

Naturalistic animal behavior is complex. Learn the practical skills of how to record and track this complex behavior using modern tools, and the pros and cons of different approaches. Discuss recent work on modeling individual and collective animal behavior while maintaining quantitative rigor, as well as the relationship between behavior and the brain. Investigate connections between behavior and neural activity in the model animal systems of nematode, fruitfly, squid, and mouse. Read and assess papers weekly. Design and complete a short project in studies of complex behavior, with support from relevant literature. Present the results of the project to the class.

Course Contents


Traditional approaches to ethology and neuroscience
Basics of data analysis, machine learning, deep neural nets
Basics of optics, computer vision, camera design

Quantifying movement

Image filtering and morphological operations
Marker based and marker-less pose estimation
2.5 dimensional imaging (RGB-D)
Semantic segmentation
Tracking collective animal behavior

Describing behavior

Eigenworms and the dynamical systems view
Mouse behavioral syllables and Markov models
Drosophila behavioral space and nonlinear dimensionality reduction
Supervised learning approaches (e.g. boosted decision trees)
Drosophila social behavior and generalized linear models

Linking behavior to neural activity

Traditional brain-behavior correlations: hippocampal cell types
Imaging neural activity in freely moving C. elegans
Selective neural activation-behavior screens in Drosophila
Cephalopod skin patterning dynamics
Mouse basal ganglia and motor control

Collective behavior

Boids model
Experimental manipulation of animal collectives
Fish schooling and deep attention networks
Noise-induced schooling


Participation in class discussions 50%, Project 50%.

Prerequisites or Prior Knowledge

Introductory neuroscience and preparation in one or more areas of linear algebra, machine learning, or behavioral ecology is recommended.

Reference Books

Deep Learning with Python, Chollet
Multiple View Geometry in Computer Vision, Hartley and Zisserman