During our more ‘Philosophical’ moments…we have all wondered about ‘Time’…its true nature…and its impact on us! But what if?...‘Time’…had similar concerns…about us? For the people of Aruhu, a tiny Himalayan village, deep in India’s ancient past, what begins as a visitation by a mysterious entity, quickly morphs into an inescapable trap…one that has left its imprint on all of human history…and the future! This is a trap that has seduced hundreds through its lure of absolute power, including the Nazis…who mount an epic expedition to unearth the source of God’s power on Earth…only to discover that the cost of absolute power…is also absolute! This is the story ‘Time’ wants to tell us…this is the story of Chronux!
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website.
MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. - The first complete volume on MATLAB focusing on neuroscience and psychology applications - Problem-based approach with many examples from neuroscience and cognitive psychology using real data - Illustrated in full color throughout - Careful tutorial approach, by authors who are award-winning educators with strong teaching experience
This book provides a comprehensive overview of the incredible advances achieved in the study of in vitro neuronal networks for use in basic and applied research. These cultures of dissociated neurons offer a perfect trade-off between complex experimental models and theoretical modeling approaches giving new opportunities for experimental design but also providing new challenges in data management and interpretation. Topics include culturing methodologies, neuroengineering techniques, stem cell derived neuronal networks, techniques for measuring network activity, and recent improvements in large-scale data analysis. The book ends with a series of case studies examining potential applications of these technologies.
Computational Intelligence (CI) has been a tremendously active area of - search for the past decade or so. There are many successful applications of CI in many sub elds of biology, including bioinformatics, computational - nomics, protein structure prediction, or neuronal systems modeling and an- ysis. However, there still are many open problems in biology that are in d- perate need of advanced and e cient computational methodologies to deal with tremendous amounts of data that those problems are plagued by. - fortunately, biology researchers are very often unaware of the abundance of computational techniques that they could put to use to help them analyze and understand the data underlying their research inquiries. On the other hand, computational intelligence practitioners are often unfamiliar with the part- ular problems that their new, state-of-the-art algorithms could be successfully applied for. The separation between the two worlds is partially caused by the use of di erent languages in these two spheres of science, but also by the relatively small number of publications devoted solely to the purpose of fac- itating the exchange of new computational algorithms and methodologies on one hand, and the needs of the biology realm on the other. The purpose of this book is to provide a medium for such an exchange of expertise and concerns. In order to achieve the goal, we have solicited cont- butions from both computational intelligence as well as biology researchers.
This two-volume set LNCS 14134 and LNCS 14135 constitutes the refereed proceedings of the 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, held in Ponta Delgada, Portugal, during June 19–21, 2023. The 108 full papers presented in this two-volume set were carefully reviewed and selected from 149 submissions. The papers in Part I are organized in topical sections on advanced topics in computational intelligence; advances in artificial neural networks; ANN HW-accelerators; applications of machine learning in biomedicine and healthcare; and applications of machine learning in time series analysis. The papers in Part II are organized in topical sections on deep learning and applications; deep learning applied to computer vision and robotics; general applications of artificial intelligence; interaction with neural systems in both health and disease; machine learning for 4.0 industry solutions; neural networks in chemistry and material characterization; ordinal classification; real world applications of BCI systems; and spiking neural networks: applications and algorithms.
The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI, and optical imaging methods. Analysis, visualization, and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists and theorists interested in brain function. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, the current volume addresses the need for a textbook in this interdisciplinary area. The book is written for a broad spectrum of readers ranging from physical scientists, mathematicians, and statisticians wishing to educate themselves about neuroscience, to biologists who would like to learn time series analysis methods in particular and refresh their mathematical and statistical knowledge in general, through self-pedagogy. It may also be used as a supplement for a quantitative course in neurobiology or as a textbook for instruction on neural signal processing. The first part of the book contains a set of essays meant to provide conceptual background which are not technical and shall be generally accessible. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The third part contains applications of data analysis techniques to the range of data sources indicated above (also available as part of the Chronux data analysis platform from http://chronux.org), and the fourth part contains special topics.
Part of the Marvel NOW! initiative! Throughout the ages, gods have been vanishing, their mortal worshippers left in chaos. NOW!, Thor follows a trail of blood that threatens to consume his past, present and future. The only hope for these ravaged worlds is for Thor to unravel the gruesome mystery of the God Butcher! In the distant past, Thor follows the bloody wake of murdered gods across the depths of space. In the present, the Thunder God discovers a forgotten cave that echoes with teh cries of tortured gods...and is shocked to find himself among them! And thousands of years from now, the last god-king of a ruined Asgard makes his final stand against the God Butcher's beserker legions. As three Thors from three eras race to stop the God Butcher, the full extent of his vicious scheme takes terrifying shape! THOR: GOD OF THUNDER VOL. 1 - THE GOD BUTCHER includes a code for a free digital copy on the Marvel Comics app (for iPhone?, iPad?, iPad Touch? & Android devices) and Marvel Digital Comics Shop. Additionally, this collection also features special augmented reality content available exclusive through the Marvel AR app - including cover recaps, behind the scenes features and more that add value to your reading experience at no additional cost. COLLECTING: Thor: God of Thunder 1-5, plus never-before-seen extras!
Collects Thor: God of Thunder #1-5. Continuing the series of graphic novels handpicked by Marvel Editorial to showcase pivotal story lines written and drawn by some of Marvel’s most acclaimed creators! A trail of blood threatens to consume Thor’s past, present and future! Throughout the ages, gods have been vanishing, their mortal worshippers left in chaos. The only hope for these ravaged worlds is for Thor to unravel the gruesome mystery of the God Butcher! In the distant past, the Thunder God discovers a forgotten cave that echoes with the cries of tortured gods — but soon finds himself among them! In the present, Thor follows the bloody wake of murdered gods across the depths of space. And thousands of years from now, the last god-king of a ruined Asgard makes his final stand against the God Butcher’s berserker legions. As three Thors from three eras race to stop the God Butcher, the full extent of his vicious scheme takes terrifying shape!
Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. The book collaborators contribute various chapters that describe results in different systems (visual, auditory, somatosensory perception, etc.) and different species (monkeys, rats, humans, etc). Concentrating on the recording and analysis of the firing of single and multiple neurons, and the analysis and recording of other integrative measures of network activity and network states—such as local field potentials or current source densities—is the basis of the introductory chapters. Provides a comprehensive and interdisciplinary approach Describes topics of interest to a wide range of researchers The book then moves forward with the description of the principles of neural coding for different functions and in different species and concludes with theoretical and modeling works describing how information processing functions are implemented. The text not only contains the most important experimental findings, but gives an overview of the main methodological aspects for studying neural coding. In addition, the book describes alternative approaches based on simulations with neural networks and in silico modeling in this highly interdisciplinary topic. It can serve as an important reference to students and professionals.
