论文标题
集成和火灾互连神经元的输入输出一致性
Input-output consistency in integrate and fire interconnected neurons
论文作者
论文摘要
串联间隔描述了神经元的输出。神经元网络中的信号传递意味着某些神经元的输出成为其他神经元的输入。输出应重现输入的主要特征，以避免变形成为其他神经元的输入，即输入，输出应表现出某种一致性。在本文中，我们考虑了一个问题：我们应该如何数学地表征输入以获得一致的输出？在这里，我们通过需要输出速度间隔的输入尾尾行为的可重复性来解释一致性。我们的答案是指具有随机完美整合和消防单元的相互联系的系统。特别是，我们表明，定期变化的矢量类是获得这种一致性的可能选择。添加了一些进一步的技术假设。
Interspike intervals describe the output of neurons. Signal transmission in a neuronal network implies that the output of some neurons becomes the input of others. The output should reproduce the main features of the input to avoid a distortion when it becomes the input of other neurons, that is input and output should exhibit some sort of consistency. In this paper, we consider the question: how should we mathematically characterize the input in order to get a consistent output? Here we interpret the consistency by requiring the reproducibility of the input tail behaviour of the interspike intervals distributions in the output. Our answer refers to a system of interconnected neurons with stochastic perfect integrate and fire units. In particular, we show that the class of regularly-varying vectors is a possible choice to obtain such consistency. Some further necessary technical hypotheses are added.
