Spike coding of the memory.

为了理解信息在大脑中通过神经元锋电位序列进行传递、存储和处理的方式，我们采取了以下两种策略：（A）选择能够预测生物神经元在各种输入电流下的锋电位序列的数学模型。为此，我们首先进行了细胞内电流注入实验大鼠皮层神经元，使用时间波动电流。我们在我校医学院建立了一个实验装置，并在四年的时间里努力实现实验技能。其次，我们开始根据我们获得的数据选择模型：我们确定了每个模型能够模仿每个神经元响应的参数值。(B)建立了一种分析在体皮层神经元锋电位序列的新方法。最近，我们发现，皮质神经元可以分为两类，根据他们的放电统计。根据发放不规则性分类的两组神经元对同一刺激表现出不同的反应。这表明每个区域的神经元可以分为不同的组，具有独特的尖峰统计和相应的功能特性。

In order to understand the way in which information is transferred, stored and processed in the brain by means of the sequences of neuronal spikes, we took the following two strategies: (A) Select mathematical models that could predict spike sequences of biological neurons for a variety of input currents. For this purpose, we first performed intracellular current injection experiments on rat cortical neurons, using temporally fluctuating currents. We have constructed an experimental setup in the medical school of our university and made efforts at achieving experimental skills for four years. Second, we started to select models according to the data we have obtained: We determined the parameter values with which each model is able to mimic the response of each neuron. (B) Establish a new method of analyzing spike sequences recorded from the cortical neurons in vivo. Recently we found that cortical neurons can be categorized into two groups in terms of their firing statistics. Two groups of neurons classified according to the spiking irregularity exhibit different responses to the same stimulus. This suggests that neurons in each area can be classified into different groups possessing unique spiking statistics and corresponding functional properties.