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首页> 外文期刊>Journal of Computational Neuroscience >Uncovering spatial topology represented by rat hippocampal population neuronal codes
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Uncovering spatial topology represented by rat hippocampal population neuronal codes

机译：揭示大鼠海马种群神经元代码表示的空间拓扑

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摘要

Hippocampal population codes play an important role in representation of spatial environment and spatial navigation. Uncovering the internal representation of hippocampal population codes will help understand neural mechanisms of the hippocampus. For instance, uncovering the patterns represented by rat hippocampus (CA1) pyramidal cells during periods of either navigation or sleep has been an active research topic over the past decades. However, previous approaches to analyze or decode firing patterns of population neurons all assume the knowledge of the place fields, which are estimated from training data a priori. The question still remains unclear how can we extract information from population neuronal responses either without a priori knowledge or in the presence of finite sampling constraint. Finding the answer to this question would leverage our ability to examine the population neuronal codes under different experimental conditions. Using rat hippocampus as a model system, we attempt to uncover the hidden "spatial topology" represented by the hippocampal population codes. We develop a hidden Markov model (HMM) and a vari-ational Bayesian (VB) inference algorithm to achieve this computational goal, and we apply the analysis to extensive simulation and experimental data. Our empirical results show promising direction for discovering structural patterns of ensemble spike activity during periods of active navigation. This study would also provide useful insights for future exploratory data analysis of population neuronal codes during periods of sleep.
机译：海马人口代码在空间环境和空间导航的表示中起着重要作用。揭示海马人口代码的内部表示形式将有助于理解海马的神经机制。例如，在过去的几十年中，发现在导航或睡眠期间由大鼠海马（CA1）锥体细胞代表的模式一直是一个活跃的研究主题。但是，先前用于分析或解码群体神经元放电模式的方法都假定了位置场的知识，该位置场是先验地根据训练数据估算的。这个问题仍然不清楚，我们如何在没有先验知识或有限采样约束的情况下如何从群体神经元反应中提取信息。找到这个问题的答案将利用我们在不同实验条件下检查种群神经元代码的能力。使用大鼠海马作为模型系统，我们试图揭示由海马种群代码表示的隐藏“空间拓扑”。我们开发了隐马尔可夫模型（HMM）和变分贝叶斯（VB）推理算法来实现此计算目标，并将该分析应用于广泛的仿真和实验数据。我们的经验结果表明，在主动导航期间发现合奏尖峰活动的结构模式有希望的方向。这项研究还将为将来在睡眠期间对人群神经元代码的探索性数据分析提供有用的见识。

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  • 来源
    《Journal of Computational Neuroscience》 |2012年第2期|p.227-255|共29页
  • 作者

    Zhe Chen; Fabian Kloosterman; Emery N. Brown; Matthew A. Wilson;

  • 作者单位

    Neuroscience Statistics Research Lab, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;

    Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;

    Neuroscience Statistics Research Lab, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA,Department of Brain and Cognitive Sciences and Harvard-MIT Division of Health and Science Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;

    Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;

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  • 正文语种 eng
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  • 关键词

    hidden markov model; expectation-maximization; variational bayesian; inference; place cells; population codes; spatial topology; force-based algorithm;

    机译：隐藏的马尔可夫模型;期望最大化贝叶斯变分推理;放置细胞;人口代码;空间拓扑基于力的算法;

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