Department of Mathematical Sciences, Loughborough University, Loughborough, LE11 3TU, UK.
Sci Rep. 2017 Dec 5;7(1):17007. doi: 10.1038/s41598-017-16994-y.
It is well known that architecturally the brain is a neural network, i.e. a collection of many relatively simple units coupled flexibly. However, it has been unclear how the possession of this architecture enables higher-level cognitive functions, which are unique to the brain. Here, we consider the brain from the viewpoint of dynamical systems theory and hypothesize that the unique feature of the brain, the self-organized plasticity of its architecture, could represent the means of enabling the self-organized plasticity of its velocity vector field. We propose that, conceptually, the principle of cognition could amount to the existence of appropriate rules governing self-organization of the velocity field of a dynamical system with an appropriate account of stimuli. To support this hypothesis, we propose a simple non-neuromorphic mathematical model with a plastic self-organized velocity field, which has no prototype in physical world. This system is shown to be capable of basic cognition, which is illustrated numerically and with musical data. Our conceptual model could provide an additional insight into the working principles of the brain. Moreover, hardware implementations of plastic velocity fields self-organizing according to various rules could pave the way to creating artificial intelligence of a novel type.
众所周知，从架构上来看，大脑是一个神经网络，即由许多相对简单的单元灵活耦合而成的集合。然而，其架构如何使其具有大脑独有的高级认知功能一直不清楚。在这里，我们从动力系统理论的角度来看待大脑，并假设其架构的自组织可塑性这一独特特征可能代表了使速度矢量场自组织的手段。我们提出，从概念上讲，认知原则可以归结为存在适当的规则来控制具有适当刺激解释的动力系统的速度场的自组织。为了支持这一假设，我们提出了一个具有可塑性自组织速度场的简单非神经形态数学模型，该模型在物理世界中没有原型。该系统被证明能够进行基本认知，这通过数值和音乐数据进行了说明。我们的概念模型可以为大脑的工作原理提供另一种见解。此外，根据各种规则自组织的可塑性速度场的硬件实现可以为新型人工智能的创建铺平道路。
