BITS: Reconfigurable and Multifunctional Behavioral Pattern Generators

BITS：可重新配置的多功能行为模式生成器

• 批准号: 0130773
• 负责人: Randall Beer
• 金额: $ 39.9万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0130773&HistoricalAwards=false
• 关键词: BITS; Reconfigurable; Multifunctional; Behavioral; Pattern

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.

我们建议发展新的理论和模型，通过理解和实现生物系统的动态可重构和多功能信息处理架构来扩展当前的计算框架。我们将通过一个跨学科的合作研究项目来解决这一挑战，该项目专注于多功能神经机械部件及其在动物中多种行为模式的重新配置。我们提出的研究的最终目标是抽象出可以最终应用于各种其他环境的一般设计原则。具体而言，我们提出了以下四个密切相关的实验和建模/理论项目：1)我们将对软体动物applysia California的摄食系统进行详细的实验分析，该系统通过其神经力学的多功能来动态地重新配置其摄食行为，以响应不断变化的环境环境。首先，我们将描述动物在远距离行为模式之间切换的条件。其次，我们将研究这些开关的神经和力学基础。2)我们将创建和分析行为模式转换的模型，作为新的设计原则的基础。首先，我们将构建相互关联的半马尔可夫模型来捕获在生物系统中观察到的行为模式重构。其次，我们将追求工程系统的系统化设计方法的发展，具有潜在的应用于机器人装配。3)我们将创建和分析多功能模式代的模型，以确定一般的设计原则。首先，我们将使用遗传算法来进化多功能神经模式生成器，可以在不同的行为模式之间切换。其次，我们将对这些模型模式生成器的“设计空间”进行详细的研究。4)我们将探索多功能神经模式生成器在模拟VLSI中的实现。首先，我们将在上述实验和理论工作的基础上开发紧凑型、低功耗的模式发生器。其次，我们将研究噪声和分量不匹配对这些网络性能的影响。