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Attractor Landscapes and Active Tracking: The Neurodynamics of Embodied Action

机译：吸引者景观和主动跟踪：具体动作的神经动力学

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摘要

Behavior is the product of three intertwining dynamics: of the world, of the body and of internal control structures. Neurodynamics focuses on the dynamics of neural control, while observing interfaces with the world and the body. From this perspective, we present a dynamical analysis of embodied recurrent neural networks evolved to control a cybernetic device that solves a problem in active tracking. For competent action selection, agents must rely on the attractor landscapes of the evolved networks. Insights into how the networks achieve this are given in terms of the network's dynamical substrate, which highlights the role of the network's inherent attractors as they change as a function of the input parameters (sensors). We introduce some terminological extensions to neurodynamics to allow for a more precise formulation of how attractor changes influence behavior generation: in particular, attractor landscapes, which are the space of all attractors accessible through coherent parametrizations of the network (input stimuli), and the meta-transient, which resolves behavior by approaching attractors as they shape-shift. We apply these concepts to the analysis of interesting behaviors of the tracking device, such as temporal contextual dependency, chaotic transitory regimes in moments of ambiguity, and implicit mapping of environmental asymmetricities in the response of the device. Finally, we discuss the relevance of the concepts introduced in terms of autonomy, learning, and modularity.
机译：行为是三个相互交织的动力的产物：世界，身体和内部控制结构。神经动力学着重于神经控制的动力学，同时观察与世界和身体的界面。从这个角度出发，我们提出了对体现的递归神经网络进行动力学分析的方法，这些递归神经网络经过进化来控制控制论的设备，从而解决了主动跟踪中的问题。为了进行胜任的行动选择，行动者必须依靠进化网络的吸引者态势。根据网络的动态底物，对网络如何实现这一点进行了深入的了解，突出了网络固有吸引子随着输入参数（传感器）的变化而发生的作用。我们为神经动力学引入了一些术语扩展，以更精确地表述吸引子的变化如何影响行为生成：特别是吸引子景观，它是通过网络（输入刺激）的连贯参数化可访问的所有吸引子的空间-瞬态，通过吸引子在形状发生变化时接近吸引子来解决行为。我们将这些概念应用于跟踪设备的有趣行为的分析，例如时间上下文相关性，歧义时刻的混沌瞬态状态以及设备响应中环境不对称的隐式映射。最后，我们讨论了在自治，学习和模块化方面引入的概念的相关性。

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