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INFERRING CHARACTERISTICS OF SENSORIMOTOR BEHAVIOR BY QUANTIFYING DYNAMICS OF ANIMAL LOCOMOTION

Leung, KaWai (2017)
Dissertation (65 pages)
Committee Chair / Thesis Adviser: Nemenman, Ilya
Committee Members: Berman, Gordon J ; Kim, Minsu ; Boettcher, Stefan ; Ryu, William S (University of Toronto);
Research Fields: Biophysics; Physics
Keywords: Sensorimotor behavior; Biophysics; Biomechanics; Computational biology; Locomotion
Program: Laney Graduate School, Physics
Permanent url: http://pid.emory.edu/ark:/25593/s23p0

Abstract

Locomotion is one of the most well-studied topics in animal behavioral studies. Many fundamental and clinical research make use of the locomotion of an animal model to explore various aspects in sensorimotor behavior. In the past, most of these studies focused on population average of a specific trait due to limitation of data collection and processing power. With recent advance in computer vision and statistical modeling techniques, it is now possible to track and analyze large amounts of behavioral data. In this thesis, I present two projects that aim to infer the characteristics of sensorimotor behavior by quantifying the dynamics of locomotion of nematode Caenorhabditis elegans and fruit fly Drosophila melanogaster, shedding light on statistical dependence between sensing and behavior.

In the first project, I investigate the possibility of inferring noxious sensory information from the behavior of Caenorhabditis elegans. I develop a statistical model to infer the heat stimulus level perceived by individual animals from their stereotyped escape responses after stimulation by an IR laser. The model allows quantification of analgesic-like effects of chemical agents or genetic mutations in the worm. At the same time, the method is able to differentiate perturbations of locomotion behavior that are beyond affecting the sensory system. With this model I propose experimental designs that allows statistically significant identification of analgesic-like effects. In the second project, I investigate the relationship of energy budget and stability of locomotion in determining the walking speed distribution of Drosophila melanogaster during aging. The locomotion stability at different age groups is estimated from video recordings using Floquet theory. I calculate the power consumption of different locomotion speed using a biomechanics model. In conclusion, the power consumption, not stability, predicts the locomotion speed distribution at different ages.

Table of Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Stereotypical escape behavior in Caenorhabditis elegans allows quantication of eective heat stimulus levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Statistical model of the heat-evoked escape . . . . . . . . . . . 11

2.2.2 Is the heat-evoked escape stereotyped?. . . . . . . . . . . . . . 14

2.2.3 Using the statistical model . . . . . . . . . . . . . . . . . . . . 18

2.3 Designing experiments: how many worms? . . . . . . . . . . . . . . . 20

2.4 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.5 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.5.1 Worm preparation and experiment design . . . . . . . . . . . . 25

2.5.2 Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.5.3 Calculating the template velocities, the covariances, and the scaling function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 Cost of transport, not stability, predicts the walking speed distribution of Drosophila melanogaster at different ages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.3.1 Estimating stability by Floquet theory. . . . . . . . . . . . . . 35

3.3.2 Stability analysis algorithm . . . . . . . . . . . . . . . . . . . . 35

3.3.3 Power consumption . . . . . . . . . . . . . . . . . . . . . . . . 37

3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.4.1 Phase estimation and synchronization . . . . . . . . . . . . . . 39

3.4.2 Stability at dierent behavioral region . . . . . . . . . . . . . . 40

3.4.3 Stability of dierent age groups. . . . . . . . . . . . . . . . . . 40

3.4.4 Relationship between speed, stability and power consumption of dierent age groups . . . . . . . . . . . . . . . . . . . . . . . . 41

3.5 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4 Summary and outlook. . . . . . . . . . . . . . . . . . . . . . . . . 46

Files

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