Integrating Decision Elements for Behavioral Switching

集成行为切换的决策元素

• 批准号: 0843621
• 负责人: Rhanor Gillette
• 金额: $ 42万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0843621&HistoricalAwards=false
• 关键词: Integrating; Decision; Elements; Behavioral; Switching

Successful animals tend to make efficient decisions in their daily activities in terms of effort spent, risk taken and benefit gained. How they do this at the level of the dynamical neuronal circuit has broad implications for the understanding of both normal and abnormal animal and human behavior. This project outlines a plan to describe how the nervous system integrates sensation, internal state and learning. In preliminary work on a simple animal model system, a simple and robust neural network model has been derived for cost-benefit decision-making. Now, investigations are aimed at explaining the neural bases for switching between orienting and avoidance motor acts, how this switch is determined by sensation, appetite and learning, and how avoidance decision may suppress feeding behavior. Studies are to be carried out with conventional electrophysiological recording methods. This project contributes to laboratory training of undergraduate and graduate students, and through its content to field classroom education in neuroethology. In these venues an effective synthetic approach, linked by content to problems in animal decision, to neuronal systems and behavior in field and lecture is important in training the next generation of systems and computational neurobiologists. The expected results will elucidate the interactions of goal-directed neuronal network circuitries to show how animals can make successful behavioral decisions that balance perception of available resources and risk against their own needs. They will provide a simple model for approaching the neural circuitry for similar value-based decision-making in more complex animals. The further potential impact of the work is to enable improved ecological and economic modeling using biologically based decision making mechanisms.

成功的动物往往在日常活动中做出有效的决定，包括付出的努力，承担的风险和获得的利益。 它们如何在动态神经元回路的水平上做到这一点，对于理解正常和异常的动物和人类行为具有广泛的意义。 这个项目概述了一个计划，描述神经系统如何整合感觉，内部状态和学习。 在简单动物模型系统的初步工作中，推导出了一个简单且强大的神经网络模型，用于成本效益决策。 现在，研究的目的是解释在定向和回避运动行为之间转换的神经基础，这种转换是如何由感觉，食欲和学习决定的，以及回避决定如何抑制进食行为。 研究将采用传统的电生理记录方法进行。 这个项目有助于本科生和研究生的实验室培训，并通过其内容，在神经行为学领域的课堂教育。 在这些场所，一种有效的综合方法，通过内容与动物决策中的问题，神经系统和行为领域和讲座联系起来，对于培养下一代系统和计算神经生物学家非常重要。 预期的结果将阐明目标导向的神经元网络回路的相互作用，以显示动物如何能够做出成功的行为决策，平衡对可用资源和风险的感知与自身需求。 他们将为更复杂的动物提供一个简单的模型，用于接近类似的基于价值的决策的神经回路。 这项工作的进一步潜在影响是使用基于生物学的决策机制来改进生态和经济建模。