NEURAL BASIS OF INTERNAL REPRESENTATION OF PLACE

描述(改编自申请人的摘要)： 空间关系为大多数适应性行为和 情节记忆的框架。它们的内部表示涉及一个 从原始感觉输入的自我中心坐标到 分配中心框架，并最终进入电机输出，这些输出也 用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 洞察高等认知过程的神经基础和影响因素 导致早期发育、衰老、脑损伤等功能异常， 疾病和药物滥用。这项研究是由一种不断发展的理论指导的 其中地标和事件被映射到2维度量 用来连接大脑表征的框架或“图表” 结构。它们的坐标是由以下几组活动发出信号的 海马区“定位”细胞。该指标基于网络体系结构 通过直线和角度自动运动将海图坐标联系起来 信息(“路径整合”)。图表是在突触中预先配置的 独立于外部输入的连接，但它们会关联 具有标志性的信息通过探索。这将使适当的 框架选择，纠正任何 路径集成系统和使用高效的类向量运算来 计算到目标的轨迹，尽管后面的计算可能是 不是在海马区进行的，而是在其新皮质靶点进行的。这一理论 使用来自大群神经元的同步记录来探索 (50-150)和空间的神经种群编码的解释 体验和行为。拟议的技术发展应 将这些数字增加到足以准确读取人口代码 关于研究计算机制所需的短时间尺度 多个脑区之间的神经集合的相互作用。海流 研究问题有：1)环境结构与空间 在海马体活动中编码的事件的上下文？2)是 明确的导航目标或最近访问位置的记忆 在海马体或新皮质中表示，前者编码在 最短路径的矢量式计算方式？3) 齿状筋膜是海马区的一个主要亚区，它的作用是什么？ 空间学习所必需的，但不是特定位置射击所必需的 海马锥体细胞？4)是海马体的可塑性机制 据推测，空间学习是由抑制 在新的环境中发生的神经元间放电？数据将是 考虑与拟议机制的数值模拟有关的问题 使用简化的尖峰神经元模型。

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.