Electrophysiology, agent-based modeling and inverse optimal control applications in neuroethology

Catanho, Marianne

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https://hdl.handle.net/2142/31205 Copy

Description

Title

Electrophysiology, agent-based modeling and inverse optimal control applications in neuroethology

Author(s)

Catanho, Marianne

Issue Date

2012-05-22T00:35:33Z

Director of Research (if dissertation) or Advisor (if thesis)

Coleman, Todd P.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Pleurobranchaea
• Decision-making
• Agent-based model
• foraging
• Pleurobranchaea californica
• Inverse Optimal Control
• Markov Processes
• Markov Chain
• Markov decision process
• Netlogo

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.

Graduation Semester

2012-05

Permalink

http://hdl.handle.net/2142/31205

Copyright and License Information

Copyright 2012 Marianne Catanho

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