基于自主活动神经网络的认知计算：一个新兴领域
Cognitive computation with autonomously active neural networks: an emerging field
摘要：
人类的大脑是自主活动的。要了解这种自我维持的神经活动的功能作用，其与感官数据输入流的相互作用，是认知系统研究中的一个重要问题，我们在这里回顾的理论建模的现状。这篇评论将开始与一个简短的概述的实验工作，连同讨论的瞬时与自我持续的神经活动在水库计算的框架。重点将放在两个典型的神经网络架构，显示持续不断的瞬态动力学：鞍点网络和网络的吸引遗迹。自激活神经网络面临着两个看似对立的需求：稳定的内部动态状态和对输入刺激的敏感性。我们表明，这种困境可以解决吸引遗迹网络的基础上竞争的神经动力学，吸引遗迹竞争的一方与对方的瞬态优势，和另一边的输入信号的动态影响。然后，无监督和局部赫布式在线学习允许系统在内部动态瞬态和感官输入流之间建立相关性。一种新兴的认知能力就是从这种设置中产生的。该系统执行在线，并在其本身，一个非线性的独立成分分析的传感器数据流，所有的时间是连续和自主活动。这个过程将感觉输入的独立成分映射到吸引子遗迹上，吸引子遗迹以这种方式获得语义意义。
英文摘要：
The human brain is autonomously active. To understand the functional role of this self-sustained neural activity, and its interplay with the sensory data input stream, is an important question in cognitive system research and we review here the present state of theoretical modelling. This review will start with a brief overview of the experimental efforts, together with a discussion of transient vs. self-sustained neural activity in the framework of reservoir computing. The main emphasis will be then on two paradigmal neural network architectures showing continuously ongoing transient-state dynamics: saddle point networks and networks of attractor relics. Self-active neural networks are confronted with two seemingly contrasting demands: a stable internal dynamical state and sensitivity to incoming stimuli. We show, that this dilemma can be solved by networks of attractor relics based on competitive neural dynamics, where the attractor relics compete on one side with each other for transient dominance, and on the other side with the dynamical influence of the input signals. Unsupervised and local Hebbian-style online learning then allows the system to build up correlations between the internal dynamical transient states and the sensory input stream. An emergent cognitive capability results from this set-up. The system performs online, and on its own, a non-linear independent component analysis of the sensory data stream, all the time being continuously and autonomously active. This process maps the independent components of the sensory input onto the attractor relics, which acquire in this way a semantic meaning.
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