Development of predictive coding networks for spatial navigation

用于空间导航的预测编码网络的发展 摘要：哺乳动物导航使用内部模型来预测时空统计规律， 环境位置的顺序。预测编码理论将大脑视为贝叶斯解释器， 计算外部刺激和世界内部模型之间的差异。越来越 应当理解，序列空间信息由集合神经元的时间序列表示， 在海马体中放电毫无例外，这些合奏模式已被调查，专门在 成年动物少数研究测量了单个海马神经元的活动， 断奶或幼年动物短暂地探索开放的野外环境。然而，空间经验被认为 由神经元整体放电的高度压缩的时间序列在内部表示， 以theta序列、重放和预播放的形式呈现的清醒和睡眠状态， 群体而不是单个神经元。我们的目标是揭示早期生命发展的原则和阶段 以及基于吸引子的压缩时间序列的成熟作为编码未来导航的先验 经验（即，预测编码），以及年龄和早期空间经验在这些过程中的作用， 在航海学习中。为了实现这一目标，我们开发了新的方法：1。以毫秒为单位进行长期记录 分辨率，从大集合的神经元（多达70同时）从海马在发展中国家 从睁眼后第一天开始自由活动和睡眠的大鼠; 2.揭示和分析预测编码 网络特性; 3.控制和限制动物先前的空间经验。成功完成本 该提案将提供独特的资源，以帮助理解皮层网络的出现和成熟 为内部生成的表示，并将提供链接和预测模型，以研究扰动， 神经发育和精神疾病如精神分裂症的神经元系综模式， 自闭症谱系障碍

Development of predictive coding networks for spatial navigation Summary: Mammalian navigation uses internal models to predict the spatial-temporal statistical regularity of the sequence of environmental locations. Predictive coding theories view the brain as Bayesian interpreter that computes the difference between the external stimuli and an internal model of the world. It is increasingly understood that sequential spatial information is represented by temporal sequences of ensemble neuronal firing in the hippocampus. Without exception, these ensemble patterns have been investigated exclusively in adult animals. A small handful of studies measured the activity of individual hippocampal neurons as pre- weanling or juvenile animals briefly explored open-field environments. However, spatial experience is believed to be internally represented by highly compressed temporal sequences of neuronal ensemble firing during awake and sleep states in the form of theta sequences, replay, and preplay, which are expressed within populations rather than single neurons. Our goal is to reveal the principles and stages of early-life development and maturation of attractor-based compressed temporal sequences as priors for encoding future navigation experiences (i.e., predictive coding), and the role age and early spatial experience play in these processes and in navigation learning. To achieve this goal, we develop new methods to: 1. Chronically record, at millisecond resolution, from large ensembles of neurons (up to 70 simultaneously) from the hippocampus in developing freely-behaving and sleeping rats from first day after eye opening; 2. Reveal and analyze predictive coding network properties; 3. Control and restrict animals’ prior spatial experience. Successful completion of this proposal will provide unique resources to help understand the emergence and maturation of cortical networks for internally-generated representations and will provide links and predictive models to study perturbations in neuronal ensemble patterns underlying neurodevelopmental and psychiatric disorders like schizophrenia and autism spectrum disorder.