Publications by authors named "I Tsuda"

Understandings of how visual hallucinations appear have been highly influenced by generative approaches, in particular Friston's Active Inference conceptualization. Their core proposition is that these phenomena occur when hallucinatory expectations outweigh actual sensory data. This imbalance occurs as the brain seeks to minimize informational free energy, a measure of the distance between predicted and actual sensory data in a stationary open system.

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  • - A 93-year-old man came to the hospital with symptoms like fever, abdominal pain, and jaundice, leading to the discovery of serious bile duct issues through CT scans.
  • - He was diagnosed with an intraductal papillary neoplasm of the bile duct, but surgery was not an option due to his age and complications from cholangitis couldn't be managed with standard procedures.
  • - Instead, doctors used a technique called endoscopic ultrasound-guided choledochoduodenostomy to create a new pathway for bile drainage and treated the tumor with argon plasma coagulation, which successfully reduced the risk of cholangitis returning.
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Herein, we briefly review the role of nicotinic acetylcholine receptors in regulating important brain activity by controlled release of acetylcholine from subcortical neuron groups, focusing on a microscopic viewpoint and considering the nonlinear dynamics of biological macromolecules associated with neuron activity and how they give rise to advanced brain functions of brain.

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Despite decades of research, we do not definitively know how people sometimes see things that are not there. Eight models of complex visual hallucinations have been published since 2000, including Deafferentation, Reality Monitoring, Perception and Attention Deficit, Activation, Input, and Modulation, Hodological, Attentional Networks, Active Inference, and Thalamocortical Dysrhythmia Default Mode Network Decoupling. Each was derived from different understandings of brain organisation.

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The ability of the brain to generate complex spatiotemporal patterns with specific timings is essential for motor learning and temporal processing. An approach that can model this function, using the spontaneous activity of a random neural network (RNN), is associated with orbital instability. We propose a simple system that learns an arbitrary time series as the linear sum of stable trajectories produced by several small network modules.

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