Scale Free Dynamics And Critical Phenomena In Cortical Activity

The brain is composed of many interconnected neurons that form a complex system, from which thought, behavior, and creativity emerge through self-organization. By studying the dynamics of this network, some basic motifs can be identified. Recent technological and computational advances have led to rapidly accumulating empirical evidence that spontaneous cortical activity exhibits scale-free and critical behavior. Multiple experiments have identified neural processes without a preferred timescale in the avalanche-like spatial propagation of activity in cortical slices and in self-similar time series of local field potentials. Even at the largest scale, scale-free behavior can be observed by looking at the power distributions of brain rhythms as observed by neuroimaging. These findings may indicate that brain dynamics are always close to critical states – a fact with important consequences for how brain accomplishes information transfer and processing. Capitalizing on analogies between the collective behavior of interacting particles in complex physical systems and interacting neurons in the cortex, concepts from non-equilibrium thermodynamics can help to understand how dynamics are organized. In particular, the concepts of phase transitions and self-organized criticality can be used to shed new light on how to interpret collective neuronal dynamics. Despite converging support for scale-free and critical dynamics in cortical activity, the implications for accompanying cognitive functions are still largely unclear. This Research Topic aims to facilitate the discussion between scientists from different backgrounds, ranging from theoretical physics, to computational neuroscience, brain imaging and neurophysiology. By stimulating interactions with the readers of Frontiers in Physiology, we hope to advance our understanding of the role of scale-freeness and criticality in organizing brain dynamics. What do these new perspectives tell us about the brain and to what extent are they relevant for our cognitive functioning? For this Research Topic, we therefore solicit reviews, original research articles, opinion and method papers, which address the principles that organize the dynamics of cortical activity. While focusing on work in the neurosciences, this Research Topic also welcomes theoretical contributions from physics or computational approaches.

Leading authorities in the field review current experimental and theoretical knowledge on criticality and brain function. The book begins by summarizing experimental evidence for criticality and self-organized criticality in the brain. Subsequently, important breakthroughs in modeling of critical neuronal circuits and how to establish self-organized criticality in the brain are described. A milestone publication, defining upcoming directions of research in this new fi eld and set to become the primary source of information on the brain and criticality.

From Brain Dynamics to the Mind: Spatiotemporal Neuroscience explores how the self and consciousness is related to neural events. Sections in the book cover existing models used to describe the mind/brain problem, recent research on brain mechanisms and processes and what they tell us about the self, consciousness and psychiatric disorders. The book presents a spatiotemporal approach to understanding the brain and the implications for artificial intelligence, novel therapies for psychiatric disorders, and for ethical, societal and philosophical issues. Pulling concepts from neuroscience, psychology and philosophy, the book presents a modern and complete look at what we know, what we can surmise, and what we may never know about the distinction between brain and mind. Reviews models of understanding the mind/brain problem Identifies neural processes involved in consciousness, sense of self and brain function Includes concepts and research from neuroscience, psychology, cognitive science and philosophy Discusses implications for AI, novel therapies for psychiatric disorders and issues of ethics Suggests experimental designs and data analyses for future research on the mind/brain issue

An introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming. MATLAB is one of the most popular programming languages for neuroscience and psychology research. Its balance of usability, visualization, and widespread use makes it one of the most powerful tools in a scientist's toolbox. In this book, Mike Cohen teaches brain scientists how to program in MATLAB, with a focus on applications most commonly used in neuroscience and psychology. Although most MATLAB tutorials will abandon users at the beginner's level, leaving them to sink or swim, MATLAB for Brain and Cognitive Scientists takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work. The book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips and tricks. The goal is to teach the reader how to program data analyses in neuroscience and psychology. Readers will learn not only how to but also how not to program, with examples of bad code that they are invited to correct or improve. Chapters end with exercises that test and develop the skills taught in each chapter. Interviews with neuroscientists and cognitive scientists who have made significant contributions their field using MATLAB appear throughout the book. MATLAB for Brain and Cognitive Scientists is an essential resource for both students and instructors, in the classroom or for independent study.

The biological basis of physiological signals is incredibly complex. While many types of research certainly appreciate molecular, cellular and systems approach to unravel overall biological complexity, in the recent decades the interest for mathematical and computational characterization of structural and functional basis underlying biological phenomena gain wide popularity among scientists. Nowadays, we witnessed wide range applications of nonlinear quantitative analysis that produced measures such as fractal dimension, power-law scaling, Hurst exponent, Lyapunov exponent, approximate entropy, sample entropy, Lempel–Ziv complexity, as well as other metrics for predictions of onset and progression of many pathological conditions, especially in the central nervous systems (CNS). In this Research Topic, we seek to bring together the recent practical and theoretical advances in the development and application of nonlinear methods or narrower fractal-based methods for characterizing the complex physiological systems at multiple levels of the organization. We will discuss the use of various complexity measures and appropriate parameters for characterizing the variety of physiological signals up to the systems level. There are multiple aims in this topic. The recent advancement in the application of nonlinear methods for both normal and pathological physiological conditions is the first. The second aim is to emphasize the more recent successful attempt to apply these methods across animal species. Finally, a comprehensive understanding of advantages and disadvantages of each method, especially between its mathematical assumptions and real-world applicability, can help to find out what is at stake regarding the above aims and to direct us toward the more fruitful application of nonlinear measures and statistics in physiology and biology in general.

This volume establishes the conceptual foundation for sustained investigation into tool development in neuroscience. Neuroscience relies on diverse and sophisticated experimental tools, and its ultimate explanatory target—our brains and hence the organ driving our behaviors—catapults the investigation of these research tools into a philosophical spotlight. The chapters in this volume integrate the currently scattered work on tool development in neuroscience into the broader philosophy of science community. They also present an accessible compendium for neuroscientists interested in the broader theoretical dimensions of their experimental practices. The chapters are divided into five thematic sections. Section 1 discusses the development of revolutionary research tools across neuroscience’s history and argues to various conclusions concerning the relationship between new research tools and theory progress in neuroscience. Section 2 shows how a focus on research tools and their development in neuroscience transforms some traditional epistemological issues and questions about knowledge production in philosophy of science. Section 3 speaks to the most general questions about the way we characterize the nature of the portion of the world that this science addresses. Section 4 discusses hybrid research tools that integrate laboratory and computational methods in exciting new ways. Finally, Section 5 extends research on tool development to the related science of genetics. The Tools of Neuroscience Experiment will be of interest to philosophers and philosophically minded scientists working at the intersection of philosophy and neuroscience.

This special issue reviews state-of-the-art approaches to the biophysical roots of cognition. These approaches appeal to the notion that cognitive capacities serve to optimize responses to changing external conditions. Crucially, this optimisation rests on the ability to predict changes in the environment, thus allowing organisms to respond pre-emptively to changes before their onset. The biophysical mechanisms that underwrite these cognitive capacities remain largely unknown; although a number of hypotheses has been advanced in systems neuroscience, biophysics and other disciplines. These hypotheses converge on the intersection of thermodynamic and information-theoretic formulations of self-organization in the brain. The latter perspective emerged when Shannon’s theory of message transmission in communication systems was used to characterise message passing between neurons. In its subsequent incarnations, the information theory approach has been integrated into computational neuroscience and the Bayesian brain framework. The thermodynamic formulation rests on a view of the brain as an aggregation of stochastic microprocessors (neurons), with subsequent appeal to the constructs of statistical mechanics and thermodynamics. In particular, the use of ensemble dynamics to elucidate the relationship between micro-scale parameters and those of the macro-scale aggregation (the brain). In general, the thermodynamic approach treats the brain as a dissipative system and seeks to represent the development and functioning of cognitive mechanisms as collective capacities that emerge in the course of self-organization. Its explicanda include energy efficiency; enabling progressively more complex cognitive operations such as long-term prediction and anticipatory planning. A cardinal example of the Bayesian brain approach is the free energy principle that explains self-organizing dynamics in the brain in terms of its predictive capabilities – and selective sampling of sensory inputs that optimise variational free energy as a proxy for Bayesian model evidence. An example of thermodynamically grounded proposals, in this issue, associates self-organization with phase transitions in neuronal state-spaces; resulting in the formation of bounded neuronal assemblies (neuronal packets). This special issue seeks a discourse between thermodynamic and informational formulations of the self-organising and self-evidencing brain. For example, could minimization of thermodynamic free energy during the formation of neuronal packets underlie minimization of variational free energy?

This book presents a novel account of the human temporal dimension called the “human temporality” and develops a special mathematical formalism for describing such an object as the human mind. One of the characteristic features of the human mind is its temporal extent. For objects of physical reality, only the present exists, which may be conceived as a point-like moment in time. In the human temporality, the past retained in the memory, the imaginary future, and the present coexist and are closely intertwined and impact one another. This book focuses on one of the fragments of the human temporality called the complex present. A detailed analysis of the classical and modern concepts has enabled the authors to put forward the idea of the multi-component structure of the present. For the concept of the complex present, the authors proposed a novel account that involves a qualitative description and a special mathematical formalism. This formalism takes into account human goal-oriented behavior and uncertainty in human perception. The present book can be interesting for theoreticians, physicists dealing with modeling systems where the human factor plays a crucial role, philosophers who are interested in applying philosophical concepts to constructing mathematical models, and psychologists whose research is related to modeling mental processes.