Superposed recurrence plots for reconstructing a common input applied to neurons
Superposed recurrence plots for reconstructing a common input applied to neurons
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DOI：
10.1103/physreve.106.034205
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发表时间：
2022-09-12
影响因子：
2.4
通讯作者：
Ikeguchi，Tohru
中科院分区：
文献类型：
--
作者：
Nomura，Ryota;Fujiwara，Kantaro;Ikeguchi，Tohru
在大脑中，共同的输入在引发神经元组装中的同步放电中起着重要作用。然而，共同的输入通常是未知的观察者。如果一个未被观察到的共同输入可以只从输出中重建出来，这将有助于理解大脑中的通信。因此，我们已经开发出一种方法来重建一个共同的输入，只有从输出的非耦合神经元模型的放电率。为此，我们提出了一个叠加递归图（SRP），包括通过使用在多个递归图之间的每个像素点的工会确定的点。当使用具有不同发射率基线的各种类型的神经元时，SRP方法可以重建公共输入，即使当使用表现出混沌响应的非耦合神经元模型时也是如此。当我们选择适当的时间窗口来计算根据波动的宽度的放电率时，SRP方法鲁棒地重建施加到神经元模型的公共输入。这些结果表明，某些信息是嵌入在射击率。这些发现可能是利用速率编码分析全脑通信的可能基础。
In the brain, common inputs play an important role in eliciting synchronous firing in the assembly of neurons. However, common inputs are usually unknown to observers. If an unobserved common input can be reconstructed only from outputs, it would be beneficial to the understanding of communication in the brain. Thus, we have developed a method for reconstructing a common input only from output firing rates of uncoupled neuron models. To this end, we propose a superposed recurrence plot (SRP) comprising points determined by using a union of points at each pixel among multiple recurrence plots. The SRP method can reconstruct a common input when using various types of neurons with different firing rate baselines, even when using uncoupled neuron models that exhibit chaotic responses. The SRP method robustly reconstructs the common input applied to the neuron models when we select adequate time windows to calculate the firing rates in accordance with the width of the fluctuations. These results suggest that certain information is embedded in the firing rate. These findings could be a possible basis for analyzing whole-brain communication utilizing rate coding.