{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/88265"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/88265","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The behavioral space of zebrafish locomotion and its neural network analog","abstract":"How simple is the underlying control mechanism for the complex locomotion of vertebrates? I explore this question for the free-swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish “eigenshapes” as axes, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative “postural space” that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a “behavioral space” using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. In addition to this, I have quantified escape response behavior of fish to acoustic stimuli. A parameter-free analysis was done on escape response fish movies and free-swimming movie together. The analysis showed a set of three eigenshapes is sufficient to construct the quantitative postural space to observe two different behaviors: ’escape response’ and free-swimming’ on same axes. With the insight into fish behavior from postural space and behavioral space, I construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.","abstract_html":"How simple is the underlying control mechanism for the complex locomotion of vertebrates? I explore this question for the free-swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish “eigenshapes” as axes, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative “postural space” that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a “behavioral space” using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. In addition to this, I have quantified escape response behavior of fish to acoustic stimuli. A parameter-free analysis was done on escape response fish movies and free-swimming movie together. The analysis showed a set of three eigenshapes is sufficient to construct the quantitative postural space to observe two different behaviors: ’escape response’ and free-swimming’ on same axes. With the insight into fish behavior from postural space and behavioral space, I construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.","abstract_has_math":false,"creators":["Girdhar, Kiran"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics & Computnl Biology","degree_department":null,"school":null,"contributors":["Chemla, Yann R.","Gruebele, Martin","Martin Gruebele","Nelson, Mark E.","Delcomyn, Fred"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-29T21:03:04Z","date_published":"2015-09-29T21:03:04Z","updated_at":"2026-07-22T22:26:31Z","subjects":["Zebrafish Swimming","Behavioral Space","Quantitative Behavior","Eigenfish","Neural Model"],"languages":["en"],"rights":["2015 Kiran Girdhar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/88265","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chemla, Yann R.","Gruebele, Martin","Martin Gruebele","Nelson, Mark E.","Delcomyn, Fred"]},{"key":"dc:creator","label":"Author","values":["Girdhar, Kiran"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-29T21:03:04Z","2017-09-30T09:15:30Z","2015-08","2015-07-14","2015-8"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics & Computnl Biology"]},{"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":["Zebrafish Swimming","Behavioral Space","Quantitative Behavior","Eigenfish","Neural Model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["2015 Kiran Girdhar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/88265"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["How simple is the underlying control mechanism for the complex locomotion of vertebrates? I explore this question for the free-swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish “eigenshapes” as axes, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative “postural space” that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a “behavioral space” using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. In addition to this, I have quantified escape response behavior of fish to acoustic stimuli. A parameter-free analysis was done on escape response fish movies and free-swimming movie together. The analysis showed a set of three eigenshapes is sufficient to construct the quantitative postural space to observe two different behaviors: ’escape response’ and free-swimming’ on same axes. With the insight into fish behavior from postural space and behavioral space, I construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-08-01","The student, Kiran Girdhar, accepted the attached license on 2015-07-10 at 18:31.","The student, Kiran Girdhar, submitted this Dissertation for approval on 2015-07-10 at 18:35.","This Dissertation was approved for publication on 2015-07-14 at 14:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8373 on 2015-09-29 at 15:05:38","Made available in DSpace on 2015-09-29T21:03:04Z (GMT). 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Clustering analysis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. In addition to this, I have quantified escape response behavior of fish to acoustic stimuli. A parameter-free analysis was done on escape response fish movies and free-swimming movie together. The analysis showed a set of three eigenshapes is sufficient to construct the quantitative postural space to observe two different behaviors: ’escape response’ and free-swimming’ on same axes. 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