{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/82487"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/82487","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The Computational Neuroethology of Weakly Electric Fish: Body Modeling, Motion Analysis, and Sensory Signal Estimation","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 behavior. 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 deployed 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 electrosensory 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 dependent 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 gymnotids. 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 in vertebrates.","abstract_html":"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 behavior. 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 deployed 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 electrosensory 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 dependent 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 gymnotids. 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 in vertebrates.","abstract_has_math":false,"creators":["MacIver, Malcolm Angus"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Neuroscience","degree_department":null,"school":null,"contributors":["Nelson, Mark E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:45:31Z","date_published":"2015-09-25T20:45:31Z","updated_at":"2026-07-22T22:26:18Z","subjects":["Biology, Neuroscience"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3017158"],"render_values":[{"text":"(MiAaPQ)AAI3017158","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/82487","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Nelson, Mark E."]},{"key":"dc:creator","label":"Author","values":["MacIver, Malcolm Angus"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:45:31Z","10000-01-01","2001"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Neuroscience"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Biology, Neuroscience"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/82487","(MiAaPQ)AAI3017158"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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 behavior. 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 deployed 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 electrosensory 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 dependent 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 gymnotids. 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. 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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 behavior. 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 deployed 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 electrosensory 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 dependent 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 gymnotids. 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. 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