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Information filtering by coincidence detection of synchronous population output: analytical approaches to the coherence function of a two-stage neural system.
Biological Cybernetics ( IF 1.9 ) Pub Date : 2020-06-24 , DOI: 10.1007/s00422-020-00838-6
Žiga Bostner 1 , Gregory Knoll 1, 2 , Benjamin Lindner 1, 2
Affiliation  

Information about time-dependent sensory stimuli is encoded in the activity of neural populations; distinct aspects of the stimulus are read out by different types of neurons: while overall information is perceived by integrator cells, so-called coincidence detector cells are driven mainly by the synchronous activity in the population that encodes predominantly high-frequency content of the input signal (high-pass information filtering). Previously, an analytically accessible statistic called the partial synchronous output was introduced as a proxy for the coincidence detector cell’s output in order to approximate its information transmission. In the first part of the current paper, we compare the information filtering properties (specifically, the coherence function) of this proxy to those of a simple coincidence detector neuron. We show that the latter’s coherence function can indeed be well-approximated by the partial synchronous output with a time scale and threshold criterion that are related approximately linearly to the membrane time constant and firing threshold of the coincidence detector cell. In the second part of the paper, we propose an alternative theory for the spectral measures (including the coherence) of the coincidence detector cell that combines linear-response theory for shot-noise driven integrate-and-fire neurons with a novel perturbation ansatz for the spectra of spike-trains driven by colored noise. We demonstrate how the variability of the synaptic weights for connections from the population to the coincidence detector can shape the information transmission of the entire two-stage system.



中文翻译:

通过同步总体输出的重合检测进行信息过滤:两阶段神经系统相干函数的分析方法。

关于时间依赖的感觉刺激的信息被编码在神经群体的活动中。刺激的不同方面被不同类型的神经元读出:整体信息被整合细胞感知,所谓的巧合检测器细胞主要由群体中的同步活动驱动,该活动主要编码输入信号的高频内容(高通信息滤波)。以前,引入了一种可解析访问的统计信息,称为部分同步输出,作为巧合检测器单元输出的代理,以近似其信息传输。在本文的第一部分中,我们将此代理的信息过滤属性(特别是相干函数)与简单的巧合检测器神经元的信息过滤属性进行了比较。我们表明,后者的相干函数确实可以通过具有时间标度和阈值标准的部分同步输出来很好地近似,该时间标度和阈值标准与重合检测器单元的膜时间常数和触发阈值近似线性相关。在本文的第二部分中,我们提出了一种用于重合检测器单元的光谱测量(包括相干性)的替代理论,该理论结合了用于散粒噪声驱动的积分和发射神经元的线性响应理论与一种新颖的扰动ansatz。由有色噪声驱动的尖峰波谱。我们演示了从人口到巧合检测器的连接的突触权重的可变性如何影响整个两阶段系统的信息传递。

更新日期:2020-06-24
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