仅通过观察神经元对估计随机神经元动力学的相互作用图
Estimating the interaction graph of stochastic neuronal dynamics by observing only pairs of neurons
摘要：
我们解决的问题，确定对相互作用的神经元的尖峰活动的观察。神经元网络由一个具有可变长度记忆的相互作用点过程系统建模。一个神经元对另一个神经元的影响可以是兴奋性的，也可以是抑制性的。为了识别相互作用的存在和性质，我们提出了一种算法，仅基于连续时隙中两个神经元的联合活动的观察。这减少了运行该算法所需的计算量和存储量，从而使该算法适合于分析真实的神经元数据集。我们得到可计算的上界的概率假阳性和假阴性检测。作为推论，我们证明了识别算法的一致性。
英文摘要：
We address the questions of identifying pairs of interacting neurons from the observation of their spiking activity. The neuronal network is modeled by a system of interacting point processes with memory of variable length. The influence of a neuron on another can be either excitatory or inhibitory. To identify the existence and the nature of an interaction we propose an algorithm based only on the observation of joint activity of the two neurons in successive time slots. This reduces the amount of computation and storage required to run the algorithm, thereby making the algorithm suitable for the analysis of real neuronal data sets. We obtain computable upper bounds for the probabilities of false positive and false negative detection. As a corollary we prove the consistency of the identification algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
