At the beginning of the 21st century, understanding the brain has become one of the final frontiers of science. Hailed as the 'most complex object in the universe' the brain still defies a complete understanding of its workings, in particular in relation to consciousness and higher brain functions. Despite enormous scientific efforts, the question how the 'mere matter' of 1011 interacting nerve cells can give rise to the inner world of our subjective feelings still remains an enigma. However, in contrast to a few decades ago, when respectable neuroscience was not expected to deal with such questions, the search for brain/mind relationships has now become the focus of intense research. The central idea of this book: to understand the brain, we need to understand its dynamics.

Experimental and theoretical approaches to global brain dynamics that draw on the latest research in the field. The consideration of time or dynamics is fundamental for all aspects of mental activity—perception, cognition, and emotion—because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field. It includes contributions from both theoreticians and experimentalists, offering an eclectic treatment of fundamental issues. Topics addressed range from experimental and computational approaches to transient brain dynamics to the free-energy principle as a global brain theory. The book concludes with a short but rigorous guide to modern nonlinear dynamics and their application to neural dynamics.

This book provides an essential overview of computational neuroscience. It addresses a broad range of aspects, from physiology to nonlinear dynamical approaches to understanding neural computation, and from the simulation of brain circuits to the development of engineering devices and platforms for neuromorphic computation. Written by leading experts in such diverse fields as neuroscience, physics, psychology, neural engineering, cognitive science and applied mathematics, the book reflects the remarkable advances that have been made in the field of computational neuroscience, an emerging discipline devoted to the study of brain functions in terms of the information-processing properties of the structures forming the nervous system. The contents build on the workshop “Nonlinear Dynamics in Computational Neuroscience: from Physics and Biology to ICT,” which was held in Torino, Italy in September 2015.

Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

Experts explore the maturation of nonlinear brain dynamics from a developmental perspective and consider the relationship of neurodevelopmental disorders to early disruption in dynamic coordination. This volume in the Strüngmann Forum Reports series explores the complex mechanisms that accompany the dynamic processes by which the brain evolves and matures. Integrating perspectives from multiple disciplines, the book identifies knowledge gaps and proposes innovative ways forward for this emerging area of cross-disciplinary study. The contributors examine maturation of nonlinear brain dynamics across systems from a developmental perspective and relate these organizing networks to the establishment of normative cognition and pathology seen in many neurodevelopmental disorders. The book looks at key mechanistic questions, including: What role does dynamic coordination play in the establishment and maintenance of brain networks and structural and functional connectivity? How are local and global functional networks assembled and transformed over normative development? To what degree do oscillatory patterns vary across development? What is the impact of critical periods, and which factors initiate and terminate such periods? It also explores the potential of new technologies and techniques to enhance understanding of normative development and to enable early identification and remediation of neurodevelopmental and neuropsychiatric disorders that may result from early disruption in dynamic coordination. Contributors Sylvain Baillet, Yehezkel Ben-Ari, April A. Benasich, Olivier Bertrand, Gyorgy Buzsáki, Alain Chédotal, Sam M. Doesburg, Gordin Fishell, Adriana Galván, Jennifer N. Gelinas, Jay Giedd, Pierre Gressens, Ileana L. Hanganu-Opatz, Rowshanak Hashemiyoon, Takao K. Hensch, Suzana Herculano-Houzel, Mark Hübener, Mark, Matthias Kaschube, Michael S. Kobor, Bryan Kolb, Thorsten Kolling, Jean-Philippe Lachaux, Ulman Lindenberger, Heiko J. Luhmann, Hannah Monyer, Sarah R. Moore, Charles A. Nelson III, Tomáš Paus, Patrick L. Purdon, Pasko Rakic, Urs Ribary, Akira Sawa, Terrence J. Sejnowski, Wolf Singer, Cheryl L. Sisk, Nicholas C. Spitzer, Michael P. Stryker, Migranka Sur, Peter J. Uhlhaas

In neurophysiology, the emphasis has been on single-unit studies for a quarter century, since the sensory work by Lettwin and coworkers and by Hubel and Wiesel, the cen tral work by Mountcastle, the motor work by the late Evarts, and so on. In recent years, however, field potentials - and a more global approach general ly - have been receiving renewed and increasing attention. This is a result of new findings made possible by technical and conceptual advances and by the confirma tion and augmentation of earlier findings that were widely ignored for being contro versial or inexplicable. To survey the state of this active field, a conference was held in West Berlin in August 1985 that attempted to cover all of the new approaches to the study of brain function. The approaches and emphases were very varied: basic and applied, electric and magnetic, EEG and EP/ERP, connectionistic and field, global and local fields, surface and multielectrode, low frequencies and high frequencies, linear and non linear. The conference comprised sessions of invited lectures, a panel session of seven speakers on "How brains may work," and a concluding survey of relevant methodologies. The conference showed that the combination of concepts, methods, and results could open up new important vistas in brain research. Included here are the proceedings of the conference, updated and revised by the authors. Several attendees who did not present papers at the conference later ac cepted my invitation to write chapters for the book.

Cortical evoked potentials are of interest primarily as tests of changing neuronal excitabilities accompanying normal brain function. The first three steps in the anal ysis of these complex waveforms are proper placement of electrodes for recording, the proper choice of electrical or sensory stimulus parameters, and the establish ment of behavioral control. The fourth is development of techniques for reliable measurement. Measurement consists of comparison of an unknown entity with a set of standard scales or dimensions having numerical attributes in preassigned degree. A physical object can be described by the dimensions of size, mass, density, etc. In addition there are dimensions such as location, velocity, weight, hardness, etc. Some of these dimensions can be complex (e. g. size depends on three or more subsidiary coordi nates), and some can be interdependent or nonorthogonal (e. g. specification of size and mass may determine density). In each dimension the unit is defined with refer ence to a standard physical entity, e. g. a unit of mass or length, and the result of measurement is expressed as an equivalence between the unknown and the sum of a specified number of units of that entity. The dimensions of a complex waveform are elementary waveforms from which that waveform can be built by simple addition. Any finite single-valued function of time is admissible. They are called basis functions (lO, 15), and they can be expressed in numeric as well as geometric form.

Humans engage in a seemingly endless variety of different behaviors, of which some are found across species, while others are conceived of as typically human. Most generally, behavior comes about through the interplay of various constraints – informational, mechanical, neural, metabolic, and so on – operating at multiple scales in space and time. Over the years, consensus has grown in the research community that, rather than investigating behavior only from bottom up, it may be also well understood in terms of concepts and laws on the phenomenological level. Such top down approach is rooted in theories of synergetics and self-organization using tools from nonlinear dynamics. The present compendium brings together scientists from all over the world that have contributed to the development of their respective fields departing from this background. It provides an introduction to deterministic as well as stochastic dynamical systems and contains applications to motor control and coordination, visual perception and illusion, as well as auditory perception in the context of speech and music.

This is an excellent introduction for graduate students and nonspecialists to the field of mathematical and computational neurosciences. The book approaches the subject via pulsed-coupled neural networks, which have at their core the lighthouse and integrate-and-fire models. These allow for highly flexible modeling of realistic synaptic activity, synchronization and spatio-temporal pattern formation. The more advanced pulse-averaged equations are discussed.

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.