In order to understand how the brain works, it is essential to know WHAT is computed by different brain systems, and HOW those computations are performed. This is the aim of Brain Computations: What and How. Pioneering in its approach, this book will be of interest to all scientists interested in brain function and how the brain works

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics.

The essence of brain function consists in how information is processed, transferred and stored. Current neurophysiological doctrine remains focused within a spike timing paradigm, but this has a limited capacity for advancing the understanding of how the brain works. This book puts forward a new model; the neuroelectrodynamic model (NED), which describes the intrinsic computational processes by the dynamics and interaction of charges. It uses established laws of physics, such as those of classical mechanics, thermodynamics and quantum physics, as the guiding principle to develop a general theoretical construct of the brain s computational model, which incorporates the neurobiology of the cells and the molecular machinery itself, along with the electrical activity in neurons, to explain experimental results and predict the organization of the system. After addressing the deficiencies of current approaches, the laws and principles required to build a new model are discussed. In addition, as well as describing experiments which provide the required link between computation and semantics, the book highlights important concepts relating the theory of information with computation and the electrical properties of neurons. The NED model is explained and expounded and several examples of its application are shown. Of interest to all those involved in the fields of neuroscience, neurophysiology, computer science and the development of artificial intelligence, NED is a step forward in understanding the mind in computational terms. IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences

The text discusses the techniques of deep learning and machine learning in the field of neuroscience, engineering approaches to study the brain structure and dynamics, convolutional networks for fast, energy-efficient neuromorphic computing, and reinforcement learning in feedback control. It showcases case studies in neural data analysis. Features: Focuses on neuron modeling, development, and direction of neural circuits to explain perception, behavior, and biologically inspired intelligent agents for decision making Showcases important aspects such as human behavior prediction using smart technologies and understanding the modeling of nervous systems Discusses nature-inspired algorithms such as swarm intelligence, ant colony optimization, and multi-agent systems Presents information-theoretic, control-theoretic, and decision-theoretic approaches in neuroscience. Includes case studies in functional magnetic resonance imaging (fMRI) and neural data analysis This reference text addresses different applications of computational neuro-sciences using artificial intelligence, deep learning, and other machine learning techniques to fine-tune the models, thereby solving the real-life problems prominently. It will further discuss important topics such as neural rehabili-tation, brain-computer interfacing, neural control, neural system analysis, and neurobiologically inspired self-monitoring systems. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, information technology, and biomedical engineering.

A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille

"A fascinating introduction" (Steven Pinker) to the science of decision-making One of the leading thinkers in the computational neuroscience revolution offers a brilliant new perspective on the mind?s decision-making process. Why do we make the choices we make? How can science explain free will? If our brains are like slow computers originally programmed for survival with goals like food, water, and sex, why do we make choices that go against our own biological best interests? Where do values come from? What role do emotions play? From how we decide what we consume to the romantic, ethical, and financial choices we make, Read Montague guides readers through a new approach to the mind that is both entertaining and illuminating.

A collection of essays on possible futures of the science of the mind.

This volume looks at the latest advancements in imaging neuroscience methods using magnetic resonance imaging (MRI) and electroencephalography (EEG) to study the healthy and diseased brain. The chapters in this book are organized into five parts. Parts One and Two cover an introduction to this field and the latest use of molecular models. Part Three explores neurophysiological methods for assessment, such as quantitative EEG and event-related potentials. Part Four discusses the advances and innovations made in computational anatomy, and Part Five addresses the challenges faced by researchers prior to the computational neuroscience to find wider translational applications in the field of psychiatry and mental health. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and comprehensive, Computational Neuroscience is a valuable tool for researchers in the psychiatry and mental health fields who want to learn more about ways to incorporate computational approaches into utility and validity of clinical methods.

We all have more knowledge than we use; even so, say the editors of this book, ignorance often governs our actions. Society continues to find ways to misuse knowledge–from manipulating information to gain political power to restricting what ideas are explored on university campuses. Thus, when some of the best minds in the country met to focus on the optimum utilization of knowledge, it was not an idle academic inquiry. In these proceedings from that conference, which was sponsored by the Academy of Independent Scholars, the contributors examine several of the key aspects of learning: the importance of knowledge in decision making, the role of our educational system and other systems in producing and disseminating knowledge, and the relationship between knowledge and the physiological, psychological, and cultural bases of the learning process. The misuse of knowledge–or the overuse of ignorance–the authors note, could threaten the existence of the entire planet, if the kind of thinking exemplified by the nuclear arms race prevails.