Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision.
Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences.
Our drive to consume—our desire for food, clothing, smart phones, and megahomes—evolved from our ancestors’ drive to survive. But the psychological and neural processes that originally evolved to guide mammals toward resources that are necessary but scarce may mislead us in modern conditions of material abundance. Such phenomena as obesity, financial bubbles, hoarding, and shopping sprees suggest a mismatch between our instinct to consume and our current environment.
In Zen-Brain Horizons, James Austin draws on his decades of experience as a neurologist and Zen practitioner to clarify the benefits of meditative training. Austin integrates classical Buddhist literature with modern brain research, exploring the horizons of a living, neural Zen.
In the last decade, the synergistic interaction of neurosurgeons, engineers, and neuroscientists, combined with new technologies, has enabled scientists to study the awake, behaving human brain directly. These developments allow cognitive processes to be characterized at unprecedented resolution: single neuron activity. Direct observation of the human brain has already led to major insights into such aspects of brain function as perception, language, sleep, learning, memory, action, imagery, volition, and consciousness.
The event-related potential (ERP) technique, in which neural responses to specific events are extracted from the EEG, provides a powerful noninvasive tool for exploring the human brain. This volume describes practical methods for ERP research along with the underlying theoretical rationale. It offers researchers and students an essential guide to designing, conducting, and analyzing ERP experiments. This second edition has been completely updated, with additional material, new chapters, and more accessible explanations.
Our awareness of time and temporal properties is a constant feature of conscious life. Subjective temporality structures and guides every aspect of behavior and cognition, distinguishing memory, perception, and anticipation. This milestone volume brings together research on temporality from leading scholars in philosophy, psychology, and neuroscience, defining a new field of interdisciplinary research.
This introduction to the structure of the central nervous system demonstrates that the best way to learn how the brain is put together is to understand something about why. It explains why the brain is put together as it is by describing basic functions and key aspects of its evolution and development. This approach makes the structure of the brain and spinal cord more comprehensible as well as more interesting and memorable. The book offers a detailed outline of the neuroanatomy of vertebrates, especially mammals, that equips students for further explorations of the field.
Evolutionary robotics (ER) aims to apply evolutionary computation techniques to the design of both real and simulated autonomous robots. The Horizons of Evolutionary Robotics offers an authoritative overview of this rapidly developing field, presenting state-of-the-art research by leading scholars. The result is a lively, expansive survey that will be of interest to computer scientists, robotics engineers, neuroscientists, and philosophers.
This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals.