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.

中文翻译：

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用于重建应用于神经元的公共输入的叠加递归图

在大脑中，共同的输入在引发神经元组装中的同步放电方面发挥着重要作用。但是，观察者通常不知道公共输入。如果一个未被观察到的公共输入只能从输出中重构出来，那将有利于理解大脑中的交流。因此，我们开发了一种仅从非耦合神经元模型的输出放电率重建公共输入的方法。为此，我们提出了一个叠加递归图（SRP），包括通过使用多个递归图中每个像素处的点的联合确定的点。当使用具有不同放电率基线的各种类型的神经元时，SRP 方法可以重建公共输入，即使在使用表现出混沌响应的非耦合神经元模型时也是如此。当我们根据波动的宽度选择足够的时间窗口来计算放电率时，SRP 方法可以稳健地重建应用于神经元模型的公共输入。这些结果表明某些信息嵌入在发射率中。这些发现可能成为利用速率编码分析全脑通信的可能基础。