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首页> 外文期刊>Journal of Computational Neuroscience >Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings
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Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings

机译：通过推断的基于前额叶皮质录音的基于连通性的模型揭示了神经装配

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摘要

We present two graphical model-based approaches to analyse the distribution of neural activities in the prefrontal cortex of behaving rats. The first method aims at identifying cell assemblies, groups of synchronously activating neurons possibly representing the units of neural coding and memory. A graphical (Ising) model distribution of snapshots of the neural activities, with an effective connectivity matrix reproducing the correlation statistics, is inferred from multi-electrode recordings, and then simulated in the presence of a virtual external drive, favoring high activity (multi-neuron) configurations. As the drive increases groups of neurons may activate together, and reveal the existence of cell assemblies. The identified groups are then showed to strongly coactivate in the neural spiking data and to be highly specific of the inferred connectivity network, which offers a sparse representation of the correlation pattern across neural cells. The second method relies on the inference of a Generalized Linear Model, in which spiking events are integrated over time by neurons through an effective connectivity matrix. The functional connectivity matrices inferred with the two approaches are compared. Sampling of the inferred GLM distribution allows us to study the spatio-temporal patterns of activation of neurons within the identified cell assemblies, particularly their activation order: the prevalence of one order with respect to the others is weak and reflects the neuron average firing rates and the strength of the largest effective connections. Other properties of the identified cell assemblies (spatial distribution of coactivation events and firing rates of coactivating neurons) are discussed.
机译：我们提出了两种基于图形模型的方法来分析行为大鼠的前额叶皮层神经活动的分布。第一种方法旨在识别细胞装配体，即同步激活的神经元组，可能代表神经编码和记忆的单位。从多电极记录中推断出神经活动快照的图形化（Ising）模型分布，并通过有效的连通性矩阵再现相关性统计数据，然后在虚拟外部驱动器的存在下进行模拟，从而有利于高活动性（多神经元）配置。随着驱动力的增加，神经元群可能一起激活，并揭示出细胞装配体的存在。然后，显示出已识别的组在神经峰值数据中强烈共激活，并且对推断的连通性网络具有高度的特异性，从而提供了跨神经细胞的相关模式的稀疏表示。第二种方法依赖于广义线性模型的推论，其中尖峰事件随时间经过神经元通过有效的连接矩阵进行积分。比较了用两种方法推断的功能连接矩阵。推断的GLM分布的采样使我们能够研究已识别的细胞组件中神经元激活的时空模式，特别是它们的激活顺序：一个顺序相对于另一个顺序的普遍性较弱，反映了神经元的平均放电速率和最大有效连接的强度。讨论了已识别的细胞组件的其他属性（共激活事件的空间分布和共激活神经元的激发速率）。

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  • 来源
    《Journal of Computational Neuroscience》 |2016年第3期|269-293|共25页
  • 作者

    Tavoni G.; Cocco S.; Monasson R.;

  • 作者单位

    Sorbonne Univ UPMC, CNRS, PSL Res, Lab Phys Stat,Ecole Normale Super, Paris, France|Sorbonne Univ UPMC, CNRS, PSL Res, Lab Phys Theor,Ecole Normale Super, Paris, France|Univ Penn, Dept Phys & Astron, Philadelphia, PA 19104 USA;

    Sorbonne Univ UPMC, CNRS, PSL Res, Lab Phys Stat,Ecole Normale Super, Paris, France;

    Sorbonne Univ UPMC, CNRS, PSL Res, Lab Phys Theor,Ecole Normale Super, Paris, France;

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  • 正文语种 eng
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  • 关键词

    Cell assemblies; Replay; Statistical inference; Ising model; Generalized linear model;

    机译：单元装配;重播;统计推断;Ising模型;广义线性模型;

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