Crook Nigel, Jin Goh Wee
School of Technology, Oxford Brookes University, Oxford, United Kingdom.
Biosystems. 2008 Oct-Nov;94(1-2):55-9. doi: 10.1016/j.biosystems.2008.05.010. Epub 2008 Jun 20.
Evidence has been found for the presence of chaotic dynamics at all levels of the mammalian brain. This has led to some searching questions about the potential role that nonlinear dynamics may have in neural information processing. We propose that chaos equips the brain with the equivalent of a kernel trick for solving hard nonlinear problems. The approach presented, which is described as nonlinear transient computation, uses the dynamics of a well known chaotic attractor. The paper provides experimental results to show that this approach can be used to solve some challenging pattern recognition tasks. The paper also offers evidence to suggest that the efficacy of nonlinear transient computation for nonlinear pattern classification is dependent only on the generic properties of chaotic attractors and is not sensitive to the particular dynamics of specific sub-regions of chaotic phase space. If, as this work suggests, nonlinear transient computation is independent of the particulars of any given chaotic attractor, then it could be offered as a possible explanation of how the chaotic dynamics that have been observed in brain structures contribute to neural information processing tasks.
已发现证据表明哺乳动物大脑的各个层面都存在混沌动力学。这引发了一些关于非线性动力学在神经信息处理中可能发挥的潜在作用的探索性问题。我们提出，混沌为大脑配备了一种类似于核技巧的能力，用于解决棘手的非线性问题。所提出的方法，被描述为非线性瞬态计算，利用了一种著名混沌吸引子的动力学特性。本文提供的实验结果表明，这种方法可用于解决一些具有挑战性的模式识别任务。本文还提供证据表明，非线性瞬态计算用于非线性模式分类的有效性仅取决于混沌吸引子的一般特性，对混沌相空间特定子区域的特定动力学不敏感。如果正如这项研究所表明的，非线性瞬态计算独立于任何给定混沌吸引子的具体细节，那么它可以作为一种可能的解释，说明在脑结构中观察到的混沌动力学如何有助于神经信息处理任务。
