Files in this item



application/pdfCatanho_Marianne.pdf (6MB)
(no description provided)PDF


Title:Electrophysiology, agent-based modeling and inverse optimal control applications in neuroethology
Author(s):Catanho, Marianne
Advisor(s):Coleman, Todd P.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Agent-based model
Pleurobranchaea californica
Inverse Optimal Control
Markov Processes
Markov Chain
Markov decision process
Abstract:Decision-making is based on the integration of sensory information, motivational states and memory, from which assessment of outcomes and optimal action selection emerge. In foraging, animals tend to make decisions that reconcile two core goals: energy maximization and time minimization. The predatory sea-slug Pleurobranchaea californica has a simple, accessible nervous system and exhibits a repertory of simple behavioral elements that constitute effective foraging strategies. In this thesis, we introduce initial findings of sensory neurons identified through intracellular staining and electrophysiology, potentially linked to central sensory processing in the Pleurobranchaea. To further expand the principles underlying sensory integration and behavioral selection, we implemented a multi-agent based NetLogo simulation to model autonomous decision-making in the predatory sea-slug Pleurobranchaea. In particular, the model incorporated cost-benefit decisions in foraging by integrating sensation, internal state and learning in the virtual agent, replicating the particular behavioral selection process of the Pleurobranchaea. Finally, we propose two Markov decision processes to model how the animal makes decisions in its environment. Given the observed behaviors, we utilize inverse optimal control to succinctly characterize a class of utility functions the animal is maximizing. This research methodology combines principles from neurophysiology, agent-based modeling, classical conditioning, Bayesian statistics and control theory to investigate foraging decisions of the Pleurobranchaea, as it integrates sensation, internal state and learning mechanisms.
Issue Date:2012-05-22
Rights Information:Copyright 2012 Marianne Catanho
Date Available in IDEALS:2012-05-22
Date Deposited:2012-05

This item appears in the following Collection(s)

Item Statistics