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M.A. Maciver's Dissertation

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M.A. Maciver's Dissertation The computational neuroethology of weakly electric fish body modeling, motion analysis, and sensory signal estimation R s R s C R s i C s 1 Z = + X prey + + s 1/ Rp 1/ Xp 1/ Zs −ρ ρ E ⋅r 1 p / w δφ(r) = fish a3 3 + ρ ρ r 1 2 p / w − t − t = 1/ v + + − 1/ v + vt vt−1e g(1 mt )(1 e ) nt − t = 1/ w wt wt−1e = − st H(vt wt ) = + ∆ ⋅ wt wt w st Malcolm Angus MacIver THE COMPUTATIONAL NEUROETHOLOGY OF WEAKLY ELECTRIC FISH: BODY MODELING, MOTION ANALYSIS, AND SENSORY SIGNAL ESTIMATION BY MALCOLM ANGUS MACIVER B.Sc., University of Toronto, 1991 M.A., University of Toronto, 1992 THESIS Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Neuroscience in the Graduate College of the University of Illinois at Urbana-Champaign, 2001 Urbana, Illinois c Copyright by Malcolm Angus MacIver, 2001 ABSTRACT Animals actively influence the content and quality of sensory information they acquire through the positioning of peripheral sensory surfaces. Investigation of how the body and brain work together for sensory acquisition is hindered by 1) the limited number of techniques for tracking sensory surfaces, few of which provide data on the position of the entire body surface, and by 2) our inability to measure the thousands of sensory afferents stimulated during behav- ior. I present research on sensory acquisition in weakly electric fish of the genus Apteronotus, where I overcame the first barrier by developing a markerless tracking system and have de- ployed a computational approach toward overcoming the second barrier. This approach allows estimation of the full sense data stream (14,000 afferents) over the course of prey capture trials. Analysis of the tracking data showed how Apteronotus modified the position of its elec- trosensory array during predatory behavior and demonstrated that the fish use a closed-loop adaptive tracking strategy to intercept prey. In addition, nonvisual detection distance was de- pendent on water conductivity, implying that detection is dominated by the electrosense and providing the first evidence for the involvement of this sense in prey capture behavior of gym- notids. An analysis of the spatiotemporal profile of the estimated sensory signal and its neural correlates shows that the signal was 0.1% of the steady-state level at the time of detection, corresponding to a change in the total spikecount across all afferents of 0.05%. Due to the regularization of the spikecount over behaviorally relevant time windows, this change may be detectable. Using a simple threshold on the total spikecount, I estimated a neural detection time and found it to be indistinguishable from the behavioral detection time within statistical uncertainty. These results will be useful for understanding the neural and behavioral principles underlying sensory acquisition.. (http://soma.npa.uiuc.edu/labs/nelson/public_resources.html). iii Dedicated with love to my parents, Les and the memory of Diana, who allowed me to avoid letting school get in the way of my education iv I owe much gratitude to my advisor Mark Nelson for his tremendous vision and intellect in guiding my thesis work. In addition, Mark has been very supportive during a number of personal trials I went through over the course of working in his lab, for which I am very grateful. Mark gave me the freedom to pursue a large variety of things—from behavior, easily the hardest part of my thesis work, to electrophysiology, fish mold making and casting, large scale in computo experiments, and data visualization with virtual reality. I’ve had an immense amount of fun doing it all. I would like to thank Noura Sharabash for her dedication and excellence in collecting much of the behavioral data. Thanks to Len Maler, Brian Rasnow, and Chris Assad for many useful conversations and inspiration over the years. Also thanks to Brian and Chris for providing the electric field data on Apteronotus. I am grateful to Stuart Levy of the National Center for Supercomputing Applications for his role in bringing the 3D fish tracking data into the virtual reality CAVE at the Beckman Institute. A special thanks to Ben Grosser and the staff of the Visualization, Media, and Imaging Laboratory at the Beckman Institute of Advanced Science and Technology for their assistance with numerous aspects of this project, from video digitizing to 3D digitizing and 3D printing. Their cutting-edge facility enabled the development of some key techniques that otherwise may have taken a few more years to come along. I would also like to extend my gratitude to the Beckman Institute. In addition to providing me with a generous one-year research assistantship to begin research into neuromechanical approaches to sensory acquisition in electric fish, the Beckman Institute has proven an ideal interdisciplinary environment for me. I developed close working relationships with a number of groups in the Beckman, relationships which likely would not have developed were it not for the opportunities for cross-disciplinary interaction afforded by the Institute. v TABLE OF CONTENTS CHAPTER PAGE 1 The philosophy and the approach .......................... 1 1.1 Summary . ................................ 1 1.2 The logic of autonomous agents ......................... 2 1.3 The different senses of active sensing . .................. 5 1.4 Sensory ecology from an alien perspective . .................. 7 1.5 Computational neuroethology . ......................... 10 1.6 Neuromechanical simulations, biomorphic robotics . .............. 11 1.7 The research goals of this thesis ......................... 13 2 An overview of weakly electric fish .......................... 15 2.1 Summary . ................................ 15 2.2 History . ................................ 16 2.3 Electroreceptors . ................................ 18 2.4 The electric field source . ......................... 21 2.5 Locomotion and body plan . ......................... 22 2.6 Electrolocation . ................................ 23 3 Body modeling and model-based tracking for neuroethology ........... 26 3.1 Summary . ................................ 26 3.2 Introduction . ................................ 27 3.3 Body modeling . ................................ 28 3.3.1 Preparing the specimen . ......................... 29 3.3.2 Posing the specimen . ......................... 30 3.3.3 Selection of moldmaking and casting compounds . ....... 30 3.3.4 Design and construction of the mold . .................. 31 3.3.5 Making the cast . ......................... 32 3.3.6 3-D digitizing . ......................... 34 3.3.7 Creating a polygonal model . .................. 35 3.4 Model-based tracking . ......................... 36 3.4.1 Infrared videography . ......................... 36 3.4.2 Video digitizing and image processing .................. 37 vi 3.4.3 Implicit image correction and 3-D reconstruction . ....... 39 3.4.4 3-D reconstruction validation . .................. 42 3.4.5 Creation of a parametric fish model . .................. 43 3.4.6 Fitting the model to images . .................. 45 3.5 Discussion . ................................ 46 3.5.1 Linking behavior to neurophysiology .................. 46 3.5.2 Other Applications . ......................... 50 3.5.3 Future Directions . ......................... 51 4 Motion analysis and effects of water conductivity .................. 53 4.1 Summary . ................................ 53 4.2 Introduction . ................................ 54 4.3 Materials and methods . ......................... 57 4.3.1 Behavioral apparatus . ......................... 57 4.3.2 Experimental protocol . ......................... 58 4.3.3 Behavioral segment selection . .................. 59 4.3.4 Behavioral data acquisition, visualization, and analysis . ....... 59 4.4 Results ....................................... 63 4.4.1 Longitudinal velocity and acceleration .................. 65 4.4.2 Detection Distance . ......................... 67 4.4.3 Prey position at time of detection . .................. 69 4.4.4 Distribution of prey “tracks” on fish surface . .............. 72 4.4.5 Detection distance and water conductivity . .............. 73 4.4.6 Roll and pitch . ......................... 75 4.4.7 Lateral tail bend and bending velocity .................. 77 4.4.8 Effects of prey displacement on prey capture behavior . ....... 79 4.4.9 Comparison between species . .................. 80 4.5 Discussion . ................................ 82 4.5.1 Candidate sensory modalities supporting prey capture in Apteronotus .83 4.5.2 Dependence of detection distance on conductivity . ....... 83 4.5.3 Functional importance of the dorsal receptor surface . ....... 89 4.5.4 Roll: evidence for an electrosensory orienting response to prey . 91 4.5.5 Backward swimming . ......................... 92 4.5.6 Tail bend . ................................ 93 4.5.7 Closed-loop control of prey capture . .................. 94 5 Sensory signal estimation ............................... 96 5.1 Summary . ................................ 96 5.2 Introduction . ................................ 98 5.3 Methods . ................................ 98 5.3.1 The high resolution fish surface model .................. 99 5.3.1.1 Using the model-based tracking data with the new model . 100 5.3.2 Populating the fish model with electroreceptors . .......102 vii 5.3.3 Estimating the electric field at the prey ..................104 5.3.4 Measurement of prey impedance . ..................110 5.3.5 Estimating the transdermal voltage . ..................111 5.3.6
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