Perturbation experiments for hippocampal local circuits with dynamic clamp and Bayesian analysis for noisy response data

我们旨在通过微扰实验和数学分析来阐明海马区局部回路的信息处理机制。首先，我们确定了一个神经元的相减模型，然后，我们试图通过分析我们所识别的相减模型来理解局部电路中的信息处理机制。(1)摄动实验动态夹具系统的研制。(2)通过动态钳摄动实验确定大鼠海马CA1区锥体神经元的随机位相减少模型。(3)利用动态钳位技术构建了由真实神经元组成的虚拟电路，并验证了所识别的随机相减法模型的有效性。(4)基于我们确定的相位减少模型，阐述了海马神经元，例如位置细胞中的信息表征。通过这些研究成果，我们成功地确保了自上而下和自下而上的理解的一致性。

We aim to elucidate the mechanism of information processing in hippocampal local circuits by using perturbation experiments and mathematical analyses. First, we identify a phase reduction model of neurons, and then, we try to understand the mechanism of information processing in local circuits by analyzing the reduction model we identified. (1) Development of a dynamic clamp system for perturbation experiments. (2) Identification of a stochastic phase reduction model of rat hippocampal CA1 pyramidal neurons by perturbation experiments with the dynamic clamp. (3) Construction of virtual circuit consisting of real neurons with the dynamic clamp and verification of the efficacy of the stochastic phase reduction model we identified. (4) Exposition of information representations in hippocampal neurons, e.g. place cells, based on the phase reduction model we identified. Through these fruits of out research, we were a success in ensuring consistency of top-down and bottom-up understandings.