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Complex Systems: Chaos and Beyond is co-authored by scientist Kunihiko Kaneko and mathematician Ichiro Tsuda. It is a book about " the study of complex systems, based on, but beyond, chaos " (p. v). The authors develop notions about types and features of chaos and applications to biological systems are highlighted. Because " complex systems are such that we… (More)
Chaotic itinerancy is universal dynamics in high-dimensional dynamical systems, showing itinerant motion among varieties of low-dimensional ordered states through high-dimensional chaos. Discovery, basic features, characterization, examples, and significance of chaotic itinerancy are surveyed. About a decade ago, chaotic itinerancy was proposed as a… (More)
We investigate the dynamic character of a network of electrotonically coupled cells consisting of class I point neurons, in terms of a finite dimensional dynamical system. We classify a subclass of class I point neurons, called class I* point neurons. Based on this classification, we use a reduced Hindmarsh-Rose (H-R) model, which consists of two dynamical… (More)
Basic problems of complex systems are outlined with an emphasis on irreducibil-ity and dynamic many-to-many correspondences. We discuss the importance of a constructive approach to artificial reality and the significance of an internal observer.
Patients with critically ischemic limbs due to maintenance hemodialysis and diabetes are increasing in number markedly in Japan. The difficulty of treating critically ischemic limbs is well recognized. Despite active medication and surgical therapy, many critically ischemic limbs are amputated. Ninety-two patients with critically ischemic limbs were treated… (More)
To adapt to changeable or unfamiliar environments, it is important that animals develop strategies for goal-directed behaviors that meet the new challenges. We used a sequential paired-association task with asymmetric reward schedule to investigate how prefrontal neurons integrate multiple already-acquired associations to predict reward. Two types of… (More)
The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum… (More)