The Brain Dynamics Toolbox is an open software tool for simulating dynamical systems in matlab. It allows custom dynamical models to be explored in an intuitive graphical application while retaining the ability to script large-scale simulations. It can be applied to initial-value problems in any domain that involves systems of Ordinary Differential Equations (ODEs), Delay Differential Equations (DDEs), Stochastic Differential Equations (SDEs) or Partial Differential Equations (PDEs). This book is for researchers and students who wish to use the toolbox to simulate their own dynamical models. It describes how to define the dynamical equations and load them into the graphical interface from where interchangeable solvers and visualisations can be applied with no additional programming effort. The source code for the final model can be published independently of the toolbox. Dr Stewart Heitmann is a Senior Staff Scientist at the Victor Chang Cardiac Research Institute. He studies cardiac arrhythmogenesis using mathematical and computational models of excitable systems. Professor Michael Breakspear is Head of the Systems Neuroscience Group at the University of Newcastle, Australia. He studies the principles of adaptive large-scale brain dynamics in health, and the impact of their disturbance in brain disorders.

The Brain Dynamics Toolbox is open-source software for simulating non-linear dynamical systems in Matlab. It is intended for students and researchers in computational neuroscience but can be applied to any domain. It specifically solves initial-value problems in systems of Ordinary Differential Equations (ODEs), Delay Differential Equations (DDEs) and Stochastic Differential Equations (SDEs). Each of which can be extended to a system of Partial Differential Equations (PDEs).The toolbox allows new dynamical systems to be rapidly prototyped and explored in an intuitive graphical application. Its hub-and-spoke software architecture allows interchangeable solver algorithms and plotting tools to be applied with no additional programming effort. Large-scale simulations can be run in user-defined scripts and the code for the model can be published independently of the toolbox.1st Prize Winner: 2018 Competition for Software on Dynamical Systems Theory and its Applications. Junior Faculty Category. Society for Industrial and Applied Mathematics (SIAM)Honourable Mention: 2019 Competition for Tutorials on Dynamical Systems Software. Junior Faculty Category. Society for Industrial and Applied Mathematics (SIAM)

The official guide to the Brain Dynamics Toolbox The Brain Dynamics Toolbox is open-source software for simulating non-linear dynamical systems in Matlab. It is aimed at students and researchers in computational neuroscience and can be applied to other problem domains too. It specifically solves initial-value problems in systems of Ordinary Differential Equations (ODEs), Delay Differential Equations (DDEs) and Stochastic Differential Equations (SDEs). These methods can also be extended to Partial Differential Equations (PDEs). The primary benefit of the toolbox is the ease with which custom models can be built and operated at all stages of the research lifecycle. Its graphical interface allows new models to be rapidly prototyped without graphical programming. Its command-line tools allow those same models to be scripted in large-scale simulations. Source code for the finished model can be published independently of the toolbox. 1st Prize Winner 2018 Competition for Software on Dynamical Systems Theory and its Applications. Postdoc/Junior Faculty Category. Society for Industrial and Applied Mathematics (SIAM) Dr Stewart Heitmann is a Senior Staff Scientist at the Victor Chang Cardiac Research Institute and Visiting Scientist at QIMR Berghofer Medical Research Institute. He combines Software Engineering with Computational and Mathematical Neuroscience. Professor Michael Breakspear is Head of the Systems Neuroscience Group at the University of Newcastle, Australia. He studies the principles of adaptive large-scale brain dynamics in health, and the impact of their disturbance in brain disorders. "A fantastic (and open) resource for all interested in simulating dynamical systems in neuroscience" -- Olaf Sporns, Indiana University

The official guide to the Brain Dynamics Toolbox.

Neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired. It is widely recognized that effective interpretation and extraction of information from such data requires quantitative modeling. However, modeling comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data. This book gives an accessible foundation to the field of computational and network modeling of neuroimaging data and is suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging. - Provides an authoritative and comprehensive overview of major modeling approaches to neuroimaging data - Written by experts, the book's chapters use a common structure to introduce, motivate, and describe a specific modeling approach used in neuroimaging - Gives insights into the similarities and differences across different modeling approaches - Analyses details of outstanding research challenges in the field

The Oxford Handbook of Event-Related Potential Components provides a detailed and comprehensive overview of the major ERP components.

Decades of brain imaging experiments have revealed important insights into the architecture of the human brain and the detailed anatomic basis for the neural dynamics supporting human cognition. However, technical restrictions of traditional brain imaging approaches including functional magnetic resonance tomography (fMRI), positron emission tomography (PET), and magnetoencephalography (MEG) severely limit participants’ movements during experiments. As a consequence, our knowledge of the neural basis of human cognition is rooted in a dissociation of human cognition from what is arguably its foremost, and certainly its evolutionarily most determinant function, organizing our behavior so as to optimize its consequences in our complex, multi-scale, and ever-changing environment. The concept of natural cognition, therefore, should not be separated from our fundamental experience and role as embodied agents acting in a complex, partly unpredictable world. To gain new insights into the brain dynamics supporting natural cognition, we must overcome restrictions of traditional brain imaging technology. First, the sensors used must be lightweight and mobile to allow monitoring of brain activity during free participant movements. New hardware technology for electroencephalography (EEG) and near infrared spectroscopy (NIRS) allows recording electrical and hemodynamic brain activity while participants are freely moving. New data-driven analysis approaches must allow separation of signals arriving at the sensors from the brain and from non-brain sources (neck muscles, eyes, heart, the electrical environment, etc.). Independent component analysis (ICA) and related blind source separation methods allow separation of brain activity from non-brain activity from data recorded during experimental paradigms that stimulate natural cognition. Imaging the precisely timed, distributed brain dynamics that support all forms of our motivated actions and interactions in both laboratory and real-world settings requires new modes of data capture and of data processing. Synchronously recording participants’ motor behavior, brain activity, and other physiology, as well as their physical environment and external events may be termed mobile brain/body imaging ('MoBI'). Joint multi-stream analysis of recorded MoBI data is a major conceptual, mathematical, and data processing challenge. This Research Topic is one result of the first international MoBI meeting in Delmenhorst Germany in September 2013. During an intense workshop researchers from all over the world presented their projects and discussed new technological developments and challenges of this new imaging approach. Several of the presentations are compiled in this Research Topic that we hope may inspire new research using the MoBI paradigm to investigate natural cognition by recording and analyzing the brain dynamics and behavior of participants performing a wide range of naturally motivated actions and interactions.

The Routledge Handbook of Second Language Acquisition and Neurolinguistics provides a comprehensive discussion of a wide range of neurocognitive and neurobiological scientific research about learning second or additional languages. It is a one-of-a-kind centralized resource that brings together research that is typically found in disperse publication venues. Eminent global scholars from various disciplines synthesize and cross-fertilize current and past neural research about second language through systematic, in-depth, and timely chapters that discuss cores issues for understanding the neurocognition of second language learning, representation, and processing. Handbook sections provide overviews of extant and emerging neuroscience methods, syntheses of neurocognitive research on second language syntax, morphosyntax, lexicon, phonology, and pragmatics, and up-to-date descriptions of theoretical approaches of the neural basis of second language learning. The volume provides additional sections that synthesize research on a variety of topics including factors that affect the neurocognition of second language, the neural mechanisms underlying second language learning, individual differences in the neurocognition of second language, as well as research on understudied languages and populations, such as sign language, child second language learners, and individuals with aphasia. This handbook will be an indispensable resource to scholars and students across a wide range of disciplines, including those interested in second language acquisition, applied linguistics, cognitive science, psychology, neuroscience, and research methodology. It should facilitate transformative connections between ideas and disciplines and lead to informative and productive paths for future research.

Handbook of Terror Management Theory provides an overview of Terror Management Theory (TMT), including critical research derived from the theory, recent research that has expanded and refined the theory, and the many ways the theory has been utilized to understand domains of human social life. The book uses TMT as a lens to help understand human relationships to nature, cultural worldviews, the self, time, the body, attachment, group identification, religion and faith, creativity, personal growth, and the brain. The first section reviews theoretical and methodological issues, the second focuses on basic research showing how TMT enhances our understanding of a wide range of phenomena, and the third section, Applications, uses TMT to solve a variety of real world problems across different disciplines and contexts, including health behavior, aging, psychopathology, terrorism, consumerism, the legal system, art and media, risk-taking, and communication theory. - Examines the three critical hypotheses behind Terror Management Theory (TMT) - Distinguishes proximal and distal responses to death-thoughts - Provides a practical toolbox for conducting TMT research - Covers the Terror Management Health Model - Discusses the neuroscience of fear and anxiety - Identifies how fear motivates consumer behavior - Relates fear of death to psychopathologies