Nonlinear Analysis in Neuroscience and Behavioral Research

Description

Although nonlinear dynamics have been mastered by physicists and mathematicians for a long time (as most physical systems are inherently nonlinear in nature), the recent successful application of nonlinear methods to modeling and predicting several evolutionary, ecological, physiological, and biochemical processes has generated great interest and enthusiasm among researchers in computational neuroscience and cognitive psychology. Additionally, in the last years it has been demonstrated that nonlinear analysis can be successfully used to model not only basic cellular and molecular data but also complex cognitive processes and behavioral interactions. The theoretical features of nonlinear systems (such unstable periodic orbits, period-doubling bifurcations and phase space dynamics) have already been successfully applied by several research groups to analyze the behavior of a variety of neuronal and cognitive processes. Additionally the concept of strange attractors has lead to a new understanding of information processing which considers higher cognitive functions (such as language, attention, memory and decision making) as complex systems emerging from the dynamic interaction between parallel streams of information flowing between highly interconnected neuronal clusters organized in a widely distributed circuit and modulated by key central nodes. Furthermore, the paradigm of self-organization derived from the nonlinear dynamics theory has offered an interesting account of the phenomenon of emergence of new complex cognitive structures from random and non-deterministic patterns, similarly to what has been previously observed in nonlinear studies of fluid dynamics. Finally, the challenges of coupling massive amount of data related to brain function generated from new research fields in experimental neuroscience (such as magnetoencephalography, optogenetics and single-cell intra-operative recordings of neuronal activity) have generated the necessity of new research strategies which incorporate complex pattern analysis as an important feature of their algorithms. Up to now nonlinear dynamics has already been successfully employed to model both basic single and multiple neurons activity (such as single-cell firing patterns, neural networks synchronization, autonomic activity, electroencephalographic measurements, and noise modulation in the cerebellum), as well as higher cognitive functions and complex psychiatric disorders. Similarly, previous experimental studies have suggested that several cognitive functions can be successfully modeled with basis on the transient activity of large-scale brain networks in the presence of noise. Such studies have demonstrated that it is possible to represent typical decision-making paradigms of neuroeconomics by dynamic models governed by ordinary differential equations with a finite number of possibilities at the decision points and basic heuristic rules which incorporate variable degrees of uncertainty. This e-book has include frontline research in computational neuroscience and cognitive psychology involving applications of nonlinear analysis, especially regarding the representation and modeling of complex neural and cognitive systems. Several experts teams around the world have provided frontline theoretical and experimental contributions (as well as reviews, perspectives and commentaries) in the fields of nonlinear modeling of cognitive systems, chaotic dynamics in computational neuroscience, fractal analysis of biological brain data, nonlinear dynamics in neural networks research, nonlinear and fuzzy logics in complex neural systems, nonlinear analysis of psychiatric disorders and dynamic modeling of sensorimotor coordination. Rather than a comprehensive compilation of the possible topics in neuroscience and cognitive research to which non-linear may be used, this e-book intends to provide some illustrative examples of the broad range of

Get the book

Amazon.com

Search the book

eBay.com

Search the book

Similar books

• 

  Why and How is the self related to the brain midline regions?

  By Georg Northoff, Niall W Duncan, Pengmin Qin

  Mind Lang. 7, 11–34. doi:10. 1111/j.1468-0017.1992.tb00195.x Greicius, M. D., Krasnow, B., Reiss, A. L., and Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl.

• 

  Social Hormones and Human Behavior: What Do We Know and Where Do We Go from Here

  By Idan Shalev, Richard Paul Ebstein

  Oxytocin (OT) and arginine vasopressin (AVP) are the paramount social hormones in mammals and accumulating evidence also strengthens the unique role of these neuropeptides also in human social behavior.

• 

  Neural Mechanisms Underlying Movement-Based Embodied Contemplative Practices

  By Laura Schmalzl, Catherine E. Kerr

  8, 52–59. doi:10.1007/s11682-013-9242-3 Maldjian, J. A., Laurienti, P. J., Kraft, R. A., and Burdette, J. H. (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets.

• 

  Augmentation of Brain Function: Facts, Fiction and Controversy: Volume I: Brain-Machine Interfaces

  By Mikhail Lebedev, Ioan Opris, Manuel F. Casanova

  Factor Classifier Training Set Dwell Threshold Fixation Type Classifier × Training Set Classifier × Dwell Threshold ... Gaze single-trial data provide much more reach information, but they lack the modalities that can be extracted from ...

• 

  Augmentation of Brain Function: Facts, Fiction and Controversy: Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

  By Mikhail Lebedev, Ioan Opris, Manuel F. Casanova

  One set of ethical issues at this point will concern barriers to delivery of BCI therapies. One barrier is likely to be economic. The cost of BCIs in the coming decades is hard to predict, as is future societal support for universal ...

• 

  Towards embodied artificial cognition: TIME is on my side

  By Yoonsuck Choe, Marc Wittmann, Michail Maniadakis

  Everyday. robotic. action: lessons. from. human. action. control. Roy de Kleijn1,2*, George Kachergis1,2 and ... most research has focused on the early and middle stages of this process, leaving action and motor control far behind.

• 

  What Determines Social Behavior? Investigating the Role of Emotions, Self-Centered Motives, and Social Norms

  By Ernst Fehr, Patrik Vuilleumier, Corrado Corradi-Dell'Acqua

  Third, the proposer, who decides how to allocate the money, always benefits from one part of the share, thus the responder never ... Neuroimaging data of the second study revealed a dissociation between the medial prefrontal cortex, ...

• 

  Visual Mismatch Negativity (vMMN): a Prediction Error Signal in the Visual Modality

  By Gabor Stefanics, István Czigler

  VMMN is typically elicited by stimuli with infrequent (deviant) features embedded in a stream of frequent (standard) stimuli, outside the focus of attention. In this research topic we aim to present vMMN as a prediction error signal.

• 

  Imaging and monitoring astrocytes in health and disease

  By Carole Escartin, Keith Murai

  This Research Topic provides a collection of cutting-edge techniques, approaches and models to study astrocytes in health and disease. It also suggests new directions to achieve discoveries on these fascinating cells.

• 

  The Cognitive Thalamus

  By Sabine Kastner, Yuri B. Saalmann

  Cognitive processing is commonly conceptualized as being restricted to the cerebral cortex.