交错形式

Based on the return map and the principle of closed vectors, a new method is proposed to extract unstable periodic orbits embedded in chaotic attractors. As examples, the UPOs embedded in chaotic attractors of Logistic, Hénon and Lorenz are extracted respectively by this method. And our results of Skewed Hénon map also be compared with Nusse's. These results suggest that this method is valid for unstable periodic orbits from period one to period infinite of arbitrary dimension chaotic system. The dynamic considerations of spiking and UPO coding for individual neuron and neural system under external periodic and chaotic exciting stimulus also be studied in this dissertation. A lot of spiking phenomena, such as synchronization, period, and chaos appear alternatively with the changing of the stimulus frequency. For the small stimulus frequency the neuron could completely convey the periodic signal in synchronous anti-phase into interspike intervals sequences. For the slow time–scale chaotic input, the output two ISI sequences are reciprocally related to input signals, and their oscillation wave shape in time course can be derived from that of the input signals variation, furthermore, the similar input sequence and order of UPOs, distribution of LES and value of KYD remain in attractors reconstructed from ISI sequences.

发现周期信号在单个神经元传递过程中，随着激励频率的改变，神经元输出的峰峰间期interspike interval时间序列呈现出周期、混沌和准周期的交错变化，特别当外加激励信号频率较低时，周期信号可以通过神经元ISI序列以反相同步的周期运动形式传递下去；同时无论是周期还是混沌激励信号，在神经系统中的传递均与其自身强度和神经元之间的耦合强度的大小密切相关；快变时间尺度的混沌激励信号在耦合的神经系统传递过程中，会造成大量基本信息的丢失；而慢变时间尺度的混沌激励信号在神经系统传递中，它的非线性特征信息，如混沌吸引子、不稳定周期轨道、Lyapunov指数谱和分形维数，会通过系统输出的ISI序列部分地重现出来，如与输入慢变时间尺度的混沌激励信号相比，神经系统输出的ISI序列具有：相似几何形状的混沌吸引子、相近的Lyapunov指数谱和分形维数、局部结构相同的不稳定周期轨道的排列方式。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。