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dc.contributor.authorArkin, Ronald C.
dc.contributor.authorFlores-Castillo, Luis R.
dc.contributor.authorCervantes-Pérez, Francisco
dc.contributor.authorWeitzenfeld, Alfredo
dc.description.abstractWe analyze a model of neuronal mechanisms underlying amphibia’s prey-catching behavior, integrating hypotheses generated within different areas of Neuroscience and studying how the efficacy of visual prey-like dummies to release toad’s prey-catching actions depends on parallel distributed processes occurring at multiple levels of temporal abstraction. First, in the scale of 100’s of msecs, changes in neuronal activity caused by the stimulus characteristics and its current spatial-temporal relationship with the toad, as well as nervous signals related to actions’ expected consequences (e.g., mouth mechanoreceptors activation after a snapping); second, signals generated during learning events happening at a temporal scale of minutes to hours; third, signals related to the course of actions, within an undetermined time scale that may last for several hours; and fourth, signals generated by changes in motivational factors (e.g., hunger, daily and yearly cycles) occurring at a much slower time scale. In addition, we analyze how in this knowledge representation, the course of actions (plan) is episodic, goal-oriented and can be modulated by learning, or by changes in the agent’s motivational state. This modulation is the outcome of accommodating information of new situations (a non catchable prey-like stimulus) into the dynamics of underlying neuronal mechanisms, in order to change the way the toad (agent) normally responds to that domain of interaction (stop yielding prey-catching behaviors towards that specific stimulus), without affecting its performance when similar situations appear in its immediate surroundings (prey-catching behaviors to real prey remain unchanged).en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBehavior-based roboticsen_US
dc.subjectDistributed artificial intelligenceen_US
dc.subjectNeuronal networksen_US
dc.subjectPrey-catching behavioren_US
dc.subjectReinforcement learningen_US
dc.subjectStimulus specific habituationen_US
dc.titleNeuronal Networks Working at Multiple Temporal Scales as a Basis for Amphibia’s Prey-Catching Behavioren_US
dc.title.alternativeMultiple Temporal Scales in Neural Net Models
dc.title.alternativeNeuronal Multiple Temporal Scales
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameInstituto Tecnológico Autónomo de México. Departamento Académico de Computación
dc.contributor.corporatenameUniversity of Pittsburgh. Dept. of Physics and Astronomy

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  • Mobile Robot Laboratory Publications [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.
  • Mobile Robot Laboratory [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.

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