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  1.  96
    Chaotic itinerancy as a dynamical basis of hermeneutics in brain and mind.Ichiro Tsuda - 1991 - World Futures 32 (2):167-184.
    We propose a new dynamical mechanism for information processing in mind and brain. We emphasize that a hermeneutic process is one of the key processes manifesting the functions of the brain and that it can be formulated as an itinerant motion in ultrahigh dimensional dynamical systems, which may give a new realm of the dynamic information processing. Our discussions are based on the notion of chaotic information processing and the observations of biological chaos.
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  2. Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems.Ichiro Tsuda - 2001 - Behavioral and Brain Sciences 24 (5):793-810.
    Using the concepts of chaotic dynamical systems, we present an interpretation of dynamic neural activity found in cortical and subcortical areas. The discovery of chaotic itinerancy in high-dimensional dynamical systems with and without a noise term has motivated a new interpretation of this dynamic neural activity, cast in terms of the high-dimensional transitory dynamics among “exotic” attractors. This interpretation is quite different from the conventional one, cast in terms of simple behavior on low-dimensional attractors. Skarda and Freeman (1987) presented evidence (...)
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  3.  9
    Mathematical description of brain dynamics in perception and action.John S. Nicolis & Ichiro Tsuda - 1999 - Journal of Consciousness Studies 6 (11-12):11-12.
    A given but otherwise random environmental time series impinging on the input of a certain biological processor passes through with overwhelming probability practically undetected. A very small percentage of environmental stimuli, though, is ‘captured’ by the processor's nonlinear dissipative operator as initial conditions, and is ‘processed’ as solutions of its dynamics. The processor, then, is in such cases instrumental in compressing or abstracting those stimuli, thereby making the external world to collapse from a previous regime of a ‘pure state’ of (...)
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  4.  36
    Chaotic itinerancy is a key to mental diversity.Ichiro Tsuda - 2004 - Behavioral and Brain Sciences 27 (4):586-587.
    Kampis proposes the study of chaotic itinerancy, pointing out its significance in domains of cognitive science and philosophy. He has discovered in the concept of chaotic itinerancy the possibility for a new dynamical approach that elucidates mental states with a physical basis. This approach may therefore provide the means to go beyond the connectionist approach. In accordance with his theory, I here highlight three issues regarding chaotic itinerancy: transitory dynamics, diversity, and self-modifying system.
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  5.  32
    Dynamic-binding theory is not plausible without chaotic oscillation.Ichiro Tsuda - 1993 - Behavioral and Brain Sciences 16 (3):475-476.
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  6.  12
    The form of chaos in the noisy brain can manifest function.Ichiro Tsuda - 1996 - Behavioral and Brain Sciences 19 (2):309-309.
    I would like to emphasize the significance of chaotic dynamics at both local and macroscopic levels in the cortex. The basic notions dealt with in this commentary will be noise-induced order, chaotic “itinerancy” and dissipative structure. Wright & Laley's theory would be partially misleading, since emergent nonlinearity rather than the linearity at even a macroscopic level can actually subserve cortical functions.
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  7.  41
    The plausibility of a chaotic brain theory.Ichiro Tsuda - 2001 - Behavioral and Brain Sciences 24 (5):829-840.
    We consider the significance of high-dimensional transitory dynamics in the brain and mind. In particular, we highlight the roles of high-dimensional chaotic dynamical systems as an “adequate language” (Gelfand 1989), which should possess both explanatory and predictive power of description. We discuss the methods of description of dynamic behavior of the brain. These methods have been adopted to capture the averaged or deterministic complexity, and further to allow for discussion of a new approach to capture the complexity of the deviation (...)
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