Neural Mechanisms of Nutritional Homeostasis

营养稳态的神经机制

• 批准号: 0110991
• 负责人: Rhanor Gillette
• 金额: $ 32万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0110991&HistoricalAwards=false
• 关键词: Neural; Mechanisms; Nutritional; Homeostasis

Simple and complex animals, including humans, are always faced with the need to decide wisely upon actions using incomplete information. How the nervous system is organized to do this has been a major puzzle. However, we know that animals make most decisions using their own affective state as information, and that in their computations they integrate their own internal state with sensation and experience to arrive at decisions. The result represents a cost-benefit analysis of a behavioral decision in terms of probable benefit, resource loss and possible self-damage. This proposal describes plans to study the neural basis of cost-benefit analysis in decision-making in foraging behavior using a model predator species with simple body form, behavior and nervous system. Three goals are outlined: 1) to find how satiation and prey-avoidance learning influence decision-making mechanisms in the nervous system for attack or avoidance; 2) to determine the role of serotonin, a critical biasing factor, in the mechanisms of decision-making; and 3) to summarize and test the results in a computational model of the neural networks. The expected results are significant to the development of autonomous robots capable of making least-probable-error decisions in a noisy environment, and to the evolution of artificial intelligence for which motivation-based processes may provide the critical regulation of goal related activity, just as in real intelligence systems. The results also relate directly to health issues of weight-control in anorexia and obesity, in that they approach the organization of processes regulating nutritional homeostasis.

无论是简单的动物还是复杂的动物，包括人类，都需要利用不完整的信息做出明智的决定。神经系统是如何组织起来做到这一点的一直是一个主要的难题。然而，我们知道，动物使用自己的情感状态作为信息来做出大多数决定，并且在它们的计算中，它们将自己的内部状态与感觉和经验结合起来以做出决定。其结果代表了一个成本效益分析的行为决策方面的可能的利益，资源损失和可能的自我损害。该建议描述了计划研究的神经基础的成本效益分析决策中的觅食行为，使用一个模型的捕食者物种具有简单的身体形态，行为和神经系统。概述了三个目标：1）发现饱足和猎物回避学习如何影响神经系统中攻击或回避的决策机制; 2）确定5-羟色胺（一个关键的偏置因素）在决策机制中的作用; 3）总结并测试神经网络计算模型中的结果。预期的结果是显着的自主机器人能够在嘈杂的环境中作出最小可能错误的决策的发展，并为人工智能的发展，基于动机的过程可能会提供关键的调节目标相关的活动，就像在真实的智能系统。这些结果也直接关系到厌食症和肥胖症中体重控制的健康问题，因为它们接近调节营养稳态的过程的组织。