NEURAL BASIS OF INTERNAL REPRESENTATION OF PLACE

地点内部表征的神经基础

• 批准号: 2714438
• 负责人: BRUCE L MCNAUGHTON
• 金额: $ 18.79万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 1984
• 资助国家: 美国
• 起止时间: 1984-03-01 至 2001-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2714438
• 关键词: action potentials; attention; behavior test; behavioral /social science research tag; brain electrical activity; electroencephalography; environmental adaptation; experimental brain lesion; hippocampus; interneurons; laboratory rat; learning; memory; microelectrodes; neural conduction; neural information processing; neural plasticity; neurophysiology; neuropsychology; orientation; single cell analysis; space perception; synapses

DESCRIPTION (Adapted from applicant's abstract) : Spatial relationships provide the context for most adaptive behaviors and the framework for episodic memory. Their internal representation involves a transformation from the egocentric coordinates of raw sensory inputs into an allocentric framework, and ultimately into motor outputs that are also represented in terms of the spatial coordinates of the goal. Mechanisms of spatial coding are highly conserved across mammals, and their study provides insight into the neural basis of higher cognitive processes and factors that contribute to abnormal function in early development, aging, brain injury, disease, and substance abuse. The research is guided by an evolving theory in which landmarks and events are mapped onto 2-dimensional metric frameworks or "charts" that are used to connect representations of brain structures. Their coordinates are signaled by the activity of groups of hippocampal "place" cells. The metric is based on a network architecture that links chart coordinates through linear and angular self-motion information ("path-integration"). Charts are preconfigured in the synaptic connections, independently of external input, but they become associated with landmark information through exploration. This enables appropriate framework selection, correction for the drift error that is inherent in any path integration system and the use of efficient, vector-like operations to compute trajectories to goals, although the latter computations may be carried out not in hippocampus, but in its neocortical targets. The theory is explored using simultaneous recordings from large groups of neurons (50-150) and the interpretation of neural population codes for spatial experiences and behaviors. Proposed technological developments should increase these numbers sufficiently for accurate reading of population codes on the short time-scales necessary to study computational mechanisms and the interaction of neural ensembles between multiple brain regions. The current research questions are: 1) How are environmental structure and the spatial context of events encoded in hippocampal activity? 2) Are either navigational goals or memories of recently visited locations explicitly represented in the hippocampus or neocortex and are the former encoded in a manner that could subserve vector-like computations of shortest routes? 3) What is the role of the fascia dentata, a major hippocampal subfield that is required for spatial learning, but not for place specific firing in hippocampal pyramidal cells? 4) Are hippocampal plasticity mechanisms that presumably underlie spatial learning modulated by the suppression of interneuron firing that occurs in novel environments? The data will be considered in relation to numerical simulations of the proposed mechanisms using simplified, spiking-neuron models.

描述（改编自申请人的摘要）： 空间关系为大多数适应性行为提供了背景 情景记忆的框架。 他们的内部代表包括 从原始感官输入的自我中心坐标转换为 同种中心框架，最终进入运动输出，这也是 以目标的空间坐标表示。 机制 空间编码在哺乳动物中高度保守，他们的研究提供了 深入了解高级认知过程和因素的神经基础 导致早期发育、衰老、脑损伤等功能异常， 疾病和药物滥用。 该研究以不断发展的理论为指导 其中地标和事件被映射到二维度量上 用于连接大脑表征的框架或“图表” 结构。 它们的坐标由群体的活动发出信号 海马“位置”细胞。 该指标基于网络架构 通过线性和角度自运动链接图表坐标 信息（“路径整合”）。 图表在突触中预先配置 连接，独立于外部输入，但它们变得相关联 通过探索获得具有里程碑意义的信息。 这使得适当的 框架选择，校正任何固有的漂移误差 路径集成系统和使用高效的、类似矢量的操作 计算目标的轨迹，尽管后面的计算可能是 不是在海马体中进行，而是在其新皮质目标中进行。 理论 使用大群神经元的同时记录进行探索 （50-150）以及空间神经群体代码的解释 经历和行为。 拟议的技术发展应 充分增加这些数字以便准确读取人口代码 在研究计算机制和必要的短时间尺度上 多个大脑区域之间神经系统的相互作用。 目前的 研究问题是：1）环境结构和空间如何？ 海马活动编码的事件背景？ 2) 是 明确的导航目标或最近访问过的位置的记忆 代表在海马体或新皮质中，前者编码在 可以促进最短路径的类似向量计算的方式吗？ 3） 齿状筋膜（海马的主要亚区）的作用是什么？ 空间学习所需，但不适用于特定地点的射击 海马锥体细胞？ 4）海马可塑性机制是 可能是通过抑制来调节空间学习的基础 在新环境中发生的中间神经元放电？ 数据将是 考虑与所提出的机制的数值模拟相关的问题 使用简化的尖峰神经元模型。