On Neuroinformatics: Mathematical Models of Neuroscience and Neurocomputing

Wang, Yingxu; Fariello, Gabriele

doi:10.1166/jama.2012.1015

Cited by 54 publications

(48 citation statements)

References 28 publications

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“…Cognitive informatics is a transdisciplinary enquiry of computer science, information science, cognitive science, and intelligence science, which investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing [38,44,50,53,57,64,68,76,80,81,84,[87][88][89][90]. Brain informatics is a joint field of brain and information sciences that studies the information processing mechanisms of the brain at the physiological level by computing and medical imagination technologies [51,52,56,60,62,65,67,69,70,79]. Brain informatics explains how the most complicated physiological system, the human brain, is formed based on complex nervous systems and neurological foundations as observed in brain anatomy and neurophysiology [7,23,65].…”

Section: Paradigms Of Complex Cognitive and Intelligent Systemsmentioning

confidence: 99%

Towards the abstract system theory of system science for cognitive and intelligent systems

Wang

2015

Complex Intell. Syst.

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Basic studies in system science explore the theories, principles, and properties of abstract and concrete systems as well as their applications in system engineering. Systems are the most complicated entities and phenomena in abstract, physical, information, cognitive, brain, and social worlds across a wide range of science and engineering disciplines. The mathematical model of a general system is embodied as a hyperstructure of the abstract system. The theoretical framework of system science is formally described by a set of algebraic operations on abstract systems known as system algebra. A set of abstract structures, properties, behaviors, and principles is rigorously formalized in contemporary system theories. Applications of the formal theories of system science in system engineering, intelligent engineering, cognitive informatics, cognitive robotics, software engineering, cognitive linguistics, and cognitive computing are demonstrated, which reveals how system structural and behavioral complexities may be efficiently reduced in system representation, modeling, analysis, synthesis, inference, and implementation.

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Section: Paradigms Of Complex Cognitive and Intelligent Systemsmentioning

confidence: 99%

Towards the abstract system theory of system science for cognitive and intelligent systems

Wang

2015

Complex Intell. Syst.

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“…After preliminary information processing on the retina, the stimuli reach the external knee body along the optic intersection of the nerve fibers. After a nerve alternation, they enter the secondary and advanced visual centers of the brain through the primary visual cortex in the occipital lobe and subsequently enter a higher level joint area located in the occipital and parietal cortices for higher level information processing [30].…”

Section: Experimental Paradigm Of Ssvepmentioning

confidence: 99%

Review of brain–computer interface based on steady‐state visual evoked potential

Liu

Zhang

Liu

et al. 2022

Brain Science Advances

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The brain–computer interface (BCI) technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life. Steady‐state visual evoked potential (SSVEP) is the most researched BCI experimental paradigm, which offers the advantages of high signal‐to‐noise ratio and short training‐time requirement by users. In a complete BCI system, the two most critical components are the experimental paradigm and decoding algorithm. However, a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies. In the present study, the transient visual evoked potential, SSVEP, and various improved SSVEP paradigms are compared and analyzed, and the problems and development bottlenecks in the experimental paradigm are finally pointed out. Subsequently, the canonical correlation analysis and various improved decoding algorithms are introduced, and the opportunities and challenges of the SSVEP decoding algorithm are discussed.

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“…Dynamical systems in discrete-time play an important role in chaos theory and mathematical modelisation of many scienti.c problems [1,2,3,4]. Re-cently, more and more attention has been paid to the synchronization of chaos(hyperchaos) in discrete-time dynamical systems, due it.s applications in se-cure communication and cryptology [5,6].…”

Section: Introductionmentioning

confidence: 99%

Synchronization and Inverse Synchronization of Some Different Dimensional Discrete-time Chaotic Dynamical Systems via Scaling Matrices

Ouannas¹

2014

IJCCMS

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In this paper, new types of synchronization and inverse synchronization are proposed for some di¤erent dimensional chaotic dynamical systems in discrete-time using scaling matrices. Based on Lyapunov stability theory and nonlinear controllers, new synchronization results are derived. Numerical simulations are used to verify the e¤ectiveness of the proposed schemes. Keyword Synchronization, inverse synchronization, chaotic dynamical systems , discrete-time, Lyapunov stability 1.Introduction Dynamical systems in discrete-time play an important role in chaos theory and mathematical modelisation of many scienti.c problems [1, 2, 3, 4]. Recently , more and more attention has been paid to the synchronization of chaos(hyperchaos) in discrete-time dynamical systems, due it.s applications in secure communication and cryptology [5, 6]. Many synchronization types have been found [7, 8, 9] and di¤erent methods are used to study synchronization of discrete-time chaotic systems [10, 11, 12]. In this paper, the proplems of synchronization with scaling matrix and it.s inverse type are studied between drive-response chaotic systems in discrete-time. Based on Lyapunov stability theory, we would like to present a constructive schemes to investigate synchronization and inverse synchronization between some typical chaotic dynamical systems with respect to scaling matrices in discrete-time with di¤erent dimensions. Because in real world all chaotic maps are described by plane equations or space systems, we restrict our study about the new chaos synchronization types to 2D and 3D discrete chaotic systems and this restriction does .n lose the generality of our main results. Firstly, anew schemes are proposed to study synchronization and inverse synchronization between the drive 2D Lorenz discrete-time system and the response 3D Wang map. Secondly, the 3D generalized Hénon map is considered as the drive system and the controlled Fold map as the response system to achieve synchronization and inverse

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On Neuroinformatics: Mathematical Models of Neuroscience and Neurocomputing

Cited by 54 publications

References 28 publications

Towards the abstract system theory of system science for cognitive and intelligent systems

Towards the abstract system theory of system science for cognitive and intelligent systems

Review of brain–computer interface based on steady‐state visual evoked potential

Synchronization and Inverse Synchronization of Some Different Dimensional Discrete-time Chaotic Dynamical Systems via Scaling Matrices

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