منابع مشابه
A unified approach to attractor reconstruction.
In the analysis of complex, nonlinear time series, scientists in a variety of disciplines have relied on a time delayed embedding of their data, i.e., attractor reconstruction. The process has focused primarily on intuitive, heuristic, and empirical arguments for selection of the key embedding parameters, delay and embedding dimension. This approach has left several longstanding, but common pro...
متن کاملAttractor networks.
An attractor network is a network of neurons with excitatory interconnections that can settle into a stable pattern of firing. This article shows how attractor networks in the cerebral cortex are important for long-term memory, short-term memory, attention, and decision making. The article then shows how the random firing of neurons can influence the stability of these networks by introducing s...
متن کاملCharacterization of the disruption of neural control strategies for dynamic fingertip forces from attractor reconstruction
The Strength-Dexterity (SD) test measures the ability of the pulps of the thumb and index finger to compress a compliant and slender spring prone to buckling at low forces (<3N). We know that factors such as aging and neurodegenerative conditions bring deteriorating physiological changes (e.g., at the level of motor cortex, cerebellum, and basal ganglia), which lead to an overall loss of dexter...
متن کاملSome Convex Functions Based Measures of Independence and Their Application to Strange Attractor Reconstruction
The classical information-theoretic measures such as the entropy and the mutual information (MI) are widely applicable to many areas in science and engineering. Csiszar generalized the entropy and the MI by using the convex functions. Recently, we proposed the grid occupancy (GO) and the quasientropy (QE) as measures of independence. The QE explicitly includes a convex function in its definitio...
متن کاملAttractor Networks
Artificial neural networks (ANNs), sometimes referred to as connectionist networks, are computational models based loosely on the neural architecture of the brain. Over the past twenty years, ANNs have proven to be a fruitful framework for modeling many aspects of cognition, including perception, attention, learning and memory, language, and executive control. A particular type of ANN, called a...
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ژورنال
عنوان ژورنال: Scholarpedia
سال: 2006
ISSN: 1941-6016
DOI: 10.4249/scholarpedia.1727