Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada.
Chaos. 2009 Sep;19(3):033135. doi: 10.1063/1.3227642.
Based on the stochastic delay differential equation (SDDE) modeling of neural networks, we propose an effective signal transmission approach along the neurons in such a network. Utilizing the linear relationship between the delay time and the variance of the SDDE system output, the transmitting side encodes a message as a modulation of the delay time and the receiving end decodes the message by tracking the delay time, which is equivalent to estimating the variance of the received signal. This signal transmission approach turns out to follow the principle of the spread spectrum technique used in wireless and wireline wideband communications but in the analog domain rather than digital. We hope the proposed method might help to explain some activities in biological systems. The idea can further be extended to engineering applications. The error performance of the communication scheme is also evaluated here.
基于神经网络的随机时滞微分方程 (SDDE) 建模，我们提出了一种在该网络中的神经元中有效传输信号的方法。利用 SDDE 系统输出的时滞与方差之间的线性关系，发送方将消息编码为对时滞的调制，接收方通过跟踪时滞来解码消息，这等效于估计接收信号的方差。这种信号传输方法遵循无线和有线宽带通信中使用的扩频技术的原理，但在模拟域中而不是数字域中。我们希望所提出的方法可以帮助解释生物系统中的一些活动。该思想可以进一步扩展到工程应用中。这里还评估了通信方案的误差性能。
