BITS: Reconfigurable and Multifunctional Behavioral Pattern Generators

Case Western Reserve University, Cleveland OH

Investigators

Abstract

EIA-0130773 Randall D. Beer-Case Western Reserve-Reconfigurable and Multifunctional Behavior Pattern Generators We propose to develop new theories and models that extend current computational frameworks by understanding and implementing the dynamically reconfigurable and multifunctional information processing architectures of biological systems. We will address this challenge through a collaborative interdisciplinary research program focusing on multifunctional neuromechanical components and their reconfiguration into multiple behavioral patterns in animals. The ultimate goal of our proposed research is to abstract general design principles that can eventually be applied in a variety of other contexts. Specifically, we propose the following four closely intertwined experimental and modeling/theoretical projects: 1)We will undertake a detailed experimental analysis of the feeding system of the mollusk Aplysia California, which dynamically reconfigures its feeding behavior in response to changing environmental circumstances, and does so through the multifuntionality of its neuromechanics. First, we will characterize the conditions under which the animal switches between distant behavioral patterns. Second, we will examine the neural and mechanical basis of these switches. 2)We will create and analyze models of behavioral pattern switching as the basis for new design principles. First, we will construct interconnected semi-Markov models to capture behavioral pattern reconfiguration observed in biological systems. Second, we will pursue the development of a systematic design methodology for engineered systems, with potential applications to robotic assembly. 3)We will create and analyze models of multifunctional pattern generations in order to identify general design principles. First, we will use genetic algorithms to evolve multifunctional neural pattern generators that can switch between distinct behavioral patterns. Second, we will undertake a detailed study of the "design space" of these model pattern generators. 4)We will explore the implementation of multifunctional neural pattern generators in analog VLSI. First, we will develop compact, low-power pattern generators based on the experimental and theoretical work proposed above. Second, we will study the effects of noise and component mismatch on the performance of these networks.