Memory networks in the hippocampal formation

描述：（改编自申请人的摘要）：认知的主要目标 神经科学的目的是了解行为和心理过程， 神经回路的生理特性。空间学习是一个突出的 研究这些问题的范式。海马体的定位细胞被认为 来构建一个环境认知地图。长期目标是 研究计划是了解位置细胞如何与相关的大脑相互作用 区域来生成空间特定的点火属性，以及这些属性如何 是不同学习形式的基础。 候选人是系里的副教授。神经生物学与解剖学 德克萨斯大学休斯顿医学院。这个部门有一个 尤其是学习和记忆的神经生物学。的 候选人目前的研究利用强大的多电极技术， 同时记录来自多个部分的神经元集合的活动， 海马结构海马体被模拟为一个关联的 网络具有两个重要属性：模式完成， 从降级的输入中检索存储的模式，以及模式分离， 使存储的类似输入的表示更加不同的能力。 实验测试将由先前的假设，模式分离 发生在齿状回，模式完成发生在CA3区。在 为了理解产生神经元的网络相互作用， 在这些领域的反应，这将是必不可少的发展计算模型 与记录数据进行比较， 为新的实验做预测研究职业奖将进一步促进 候选人的职业发展，使他能够投入时间学习 并将当前的建模技术应用于本文的实验 提议研究职业发展计划既需要课程工作， 动手开发模型来分析数据，从而大大提高了 候选人长期工作的整体生产力和重要性 研究计划。这些实验将产生基本的见解， 神经相互作用的基础学习和记忆，以及洞察到 这些机制在老年痴呆症等衰弱性疾病中是如何出错的 疾病

DESCRIPTION: (Adapted from applicant's abstract): A major goal of cognitive neuroscience is to understand behavior and mental processes in terms of the physiological properties of neural circuits. Spatial learning is a prominent paradigm for studying these issues. Place cells of the hippocampus are thought to constitute a cognitive map of the environment. The long-term goal of this research program is to understand how place cells interact with related brain areas to generate spatially specific firing properties and how these properties underlie different forms of learning. The candidate is an Asst. Professor in the Dept. of Neurobiology & Anatomy at the University of Texas-Houston Medical School. This department has a particularly strong focus in the neurobiology of learning and memory. The candidate's current research utilizes powerful multi-electrode techniques to record simultaneously the activity of neuronal ensembles from multiple parts of the hippocampal formation. The hippocampus has been modeled as an associative network with two important properties: pattern completion, the ability to retrieve a stored pattern from degraded input, and pattern separation, the ability to make stored representations of similar inputs more dissimilar. Experimental tests will be made of prior hypotheses that pattern separation occurs in the dentate gyms and pattern completion occurs in the CA3 field. In order to understand the network interactions that generate the neuronal responses in these areas, it will be essential to develop computational models of different neuronal architectures to compare with the recording data and to make predictions for new experiments. A Research Career Award would further the candidate's career development by enabling him to devote time toward learning and applying current modeling techniques toward the experiments in this proposal. The research career development plan entails both course work and hands-on development of models to analyze the data, thus greatly enhancing the overall productivity and significance of the candidate's long-term research-program. These experiments will generate fundamental insights into the neural interactions that underlie learning and memory, as well as insights into how these mechanisms go awry in debilitating diseases such as Alzheimer's Disease.