Harter Derek, Kozma Robert
Division of Computer Science, University of Memphis, TN 38152, USA.
IEEE Trans Neural Netw. 2005 May;16(3):565-79. doi: 10.1109/TNN.2005.845086.
Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limit-cycle approximation allow. Here we present a discretized model inspired by Freeman's K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multilayer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.
介观层面神经动力学研究神经群体的集体动力学行为。此类模型在理解大规模脑过程中变得越来越重要。大脑呈现出非周期性振荡，其动力学行为比定点和极限环近似所允许的要丰富得多。在此，我们提出一个受弗里曼K集介观层面群体模型启发的离散模型。我们表明，这个版本能够复制非周期性/混沌神经动力学的重要原理，同时速度足够快，可用于实时自主智能体应用。K模型的这种简化不仅在效率方面提供了许多优势，而且在简单性以及根据其动力学特性进行分析的能力方面也具有优势。我们使用感知脑区神经架构的多层、高度循环模型来研究离散版本。我们利用这种架构在自主智能体中开发示例动作选择机制。
