Information processing using stable and unstable oscillations: a tutorial

Patrick Thiran, M. Hasler
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引用次数: 8

Abstract

We review some principles for information storage and processing, based on oscillations in dynamical systems. Oscillations and chaos are present in both biological and artificial neurons. A single biological neuron has an oscillatory dynamics, and can generate chaos. At a macroscopic level however, chaos is not created by the dynamics of individual neurons, but by the interaction of large groups of neurons. These macroscopic oscillations are measured by EEG recordings that indicate the presence of chaotic attractors in the brain. Also in the visual cortex, neurons have been found to oscillate in a coherent way depending on the global stimulus. On the other hand, as recurrent artificial neural networks are non linear dynamical systems, it is possible to get different behaviours by adjusting their parameters: convergence toward equilibrium points, toward periodic solutions or chaotic trajectories. In this case, the study of oscillations is more a scientific activity than a goal for storing and processing information. In this paper, however, we explore the possibilities to make use of chaos for information storage.<>
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信息处理使用稳定和不稳定的振荡:教程
本文综述了基于动态系统振荡的信息存储和处理的一些原理。振荡和混沌存在于生物和人工神经元中。单个生物神经元具有振荡动力学,可以产生混沌。然而,在宏观层面上,混沌不是由单个神经元的动态产生的,而是由大群神经元的相互作用产生的。这些宏观振荡是通过脑电图记录来测量的,脑电图记录表明大脑中存在混沌吸引子。同样在视觉皮层,神经元也被发现以一种连贯的方式振荡,依赖于全局刺激。另一方面,由于递归人工神经网络是非线性动态系统,通过调整其参数可以获得不同的行为:收敛于平衡点,收敛于周期解或混沌轨迹。在这种情况下,对振荡的研究更像是一项科学活动,而不是存储和处理信息的目标。然而,在本文中,我们探索了利用混沌进行信息存储的可能性
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