Swain Peter S, Longtin André
Centre for Non-linear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada.
Chaos. 2006 Jun;16(2):026101. doi: 10.1063/1.2213613.
Both neural and genetic networks are significantly noisy, and stochastic effects in both cases ultimately arise from molecular events. Nevertheless, a gulf exists between the two fields, with researchers in one often being unaware of similar work in the other. In this Special Issue, we focus on bridging this gap and present a collection of papers from both fields together. For each field, the networks studied range from just a single gene or neuron to endogenous networks. In this introductory article, we describe the sources of noise in both genetic and neural systems. We discuss the modeling techniques in each area and point out similarities. We hope that, by reading both sets of papers, ideas developed in one field will give insight to scientists from the other and that a common language and methodology will develop.
神经网络和遗传网络都存在显著的噪声，而且这两种情况下的随机效应最终都源于分子事件。然而，这两个领域之间存在鸿沟，一个领域的研究人员往往对另一个领域的类似工作并不了解。在本期特刊中，我们致力于弥合这一差距，同时呈现来自这两个领域的一系列论文。对于每个领域，所研究的网络范围从单个基因或神经元到内源性网络。在这篇介绍性文章中，我们描述了遗传系统和神经系统中噪声的来源。我们讨论了每个领域的建模技术并指出了相似之处。我们希望，通过阅读这两组论文，一个领域中所形成的观点能为另一个领域的科学家提供见解，并且能发展出一种通用的语言和方法。
