An argument that the complexities of brain function can be understood hierarchically, in terms of different levels of abstraction, as silicon computing is. The vast differences between the brain's neural circuitry and a computer's silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its computation is factored into different levels of abstraction. Drawing on several decades of progress in computational neuroscience, together with recent results in Bayesian and reinforcement learning methodologies, Ballard factors the brain's principal computational issues in terms of their natural place in an overall hierarchy. Each of these factors leads to a fresh perspective. A neural level focuses on the basic forebrain functions and shows how processing demands dictate the extensive use of timing-based circuitry and an overall organization of tabular memories. An embodiment level organization works in reverse, making extensive use of multiplexing and on-demand processing to achieve fast parallel computation. An awareness level focuses on the brain's representations of emotion, attention and consciousness, showing that they can operate with great economy in the context of the neural and embodiment substrates.

A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.

In the near future, we will see an increase in the development and use of all sorts of AI applications. Some of the more promising areas will be Finance, Healthcare, IoT, Manufacturing, Journalism, and Cybersecurity. Many of these applications generate a great amount of complex information. Natural Language Understanding is one of the most clear examples. Traditional ways of visualizing complex information, namely linear text, web pages and hyperlink-based applications, have serious productivity problems. Users need a lot of time to visualize the information and have problems seeing the whole picture of the results. Mind mapping is probably the only way of reducing the problems inherent in these traditional ways of visualizing complex information. Most people have no clear idea about the advantages of mind mapping or the problems created by the traditional ways of visualizing complex information. The goal of Mind Mapping and Artificial Intelligence is to provide readers with an introduction to mind mapping and artificial intelligence, to the problems of using traditional ways of visualizing complex information and as an introduction to mind mapping automation and its integration into Artificial Intelligence applications such as NLU and others. As more applications of Artificial Intelligence are developed in the near future, the need for the improvement of the visualization of the information generated will increase exponentially. Information overload will soon also happen in AI applications. This will diminish the advantages of using AI. Author José Maria Guerrero is a long-time expert in mind mapping and visualization techniques. In this book he also introduces readers to MindManager mind mapping software, which can considerably reduce the problems associated with the interpretation of complex information generated by Artificial Intelligence software. - Provides coverage of the fundamentals of mind mapping and visualization applied to Artificial Intelligence applications - Includes coverage of the scientific bases for mind mapping for the visualization of complex information - Introduces MindManager software for mind mapping - Introduces the author's MindManager toolkit for the readers to use in development of new mind mapping applications - Includes case studies and real-world applications of MindManager for AI applications, including examples using IBM Watson NLU

An exciting, new framework for interpreting the philosophical significance of neuroscience. All science needs to simplify, but when the object of research is something as complicated as the brain, this challenge can stretch the limits of scientific possibility. In fact, in The Brain Abstracted, an avowedly “opinionated” history of neuroscience, M. Chirimuuta argues that, due to the brain’s complexity, neuroscientific theories have only captured partial truths—and “neurophilosophy” is unlikely to be achieved. Looking at the theory and practice of neuroscience, both past and present, Chirimuuta shows how the science has been shaped by the problem of brain complexity and the need, in science, to make things as simple as possible. From this history, Chirimuuta draws lessons for debates in philosophy of science over the limits and definition of science and in philosophy of mind over explanations of consciousness and the mind-body problem. The Brain Abstracted is the product of a historical rupture that has become visible in the twenty-first century, between the “classical” scientific approach, which seeks simple, intelligible principles underlying the manifest complexity of nature, and a data-driven engineering approach, which dispenses with the search for elegant, explanatory laws and models. In the space created by this rupture, Chirimuuta finds grounds for theoretical and practical humility. Her aim in The Brain Abstracted is not to reform neuroscience, or offer advice to neuroscientists, but rather to interpret their work—and to suggest a new framework for interpreting the philosophical significance of neuroscience.

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

An "elegant", "engrossing" (Carol Tavris, Wall Street Journal) examination of what we think we know about the brain and why -- despite technological advances -- the workings of our most essential organ remain a mystery. "I cannot recommend this book strongly enough."--Henry Marsh, author of Do No Harm For thousands of years, thinkers and scientists have tried to understand what the brain does. Yet, despite the astonishing discoveries of science, we still have only the vaguest idea of how the brain works. In The Idea of the Brain, scientist and historian Matthew Cobb traces how our conception of the brain has evolved over the centuries. Although it might seem to be a story of ever-increasing knowledge of biology, Cobb shows how our ideas about the brain have been shaped by each era's most significant technologies. Today we might think the brain is like a supercomputer. In the past, it has been compared to a telegraph, a telephone exchange, or some kind of hydraulic system. What will we think the brain is like tomorrow, when new technology arises? The result is an essential read for anyone interested in the complex processes that drive science and the forces that have shaped our marvelous brains.

An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between biological and artificial neural networks. Examples with real neural data demonstrate how computational models can be used to understand phenomena observed in neural recordings. Based on years of classroom experience, the material has been carefully streamlined to provide all the content needed to build a foundation for modeling neural circuits in a one-semester course. Proven in the classroom Example-rich, student-friendly approach Includes Python code and a mathematical appendix reviewing the requisite background in calculus, linear algebra, and probability Ideal for engineering, science, and mathematics majors and for self-study

Is there a right way to study how the brain works? Following the empiricist's tradition, the most common approach involves the study of neural reactions to stimuli presented by an experimenter. This 'outside-in' method fueled a generation of brain research and now must confront hidden assumptions about causation and concepts that may not hold neatly for systems that act and react. György Buzsáki's The Brain from Inside Out examines why the outside-in framework for understanding brain function has become stagnant and points to new directions for understanding neural function. Building upon the success of 2011's Rhythms of the Brain, Professor Buzsáki presents the brain as a foretelling device that interacts with its environment through action and the examination of action's consequence. Consider that our brains are initially filled with nonsense patterns, all of which are gibberish until grounded by action-based interactions. By matching these nonsense "words" to the outcomes of action, they acquire meaning. Once its circuits are "calibrated" by action and experience, the brain can disengage from its sensors and actuators, and examine "what happens if" scenarios by peeking into its own computation, a process that we refer to as cognition. The Brain from Inside Out explains why our brain is not an information-absorbing coding device, as it is often portrayed, but a venture-seeking explorer constantly controlling the body to test hypotheses. Our brain does not process information: it creates it.

A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried out in the ventral stream. This book develops a mathematical framework that describes learning of invariant representations of the ventral stream and is particularly relevant to deep convolutional learning networks. The authors propose a theory based on the hypothesis that the main computational goal of the ventral stream is to compute neural representations of images that are invariant to transformations commonly encountered in the visual environment and are learned from unsupervised experience. They describe a general theoretical framework of a computational theory of invariance (with details and proofs offered in appendixes) and then review the application of the theory to the feedforward path of the ventral stream in the primate visual cortex.

A Blueprint for the Hard Problem of Consciousness addresses the fundamental mechanism that allows physical events to transcend into subjective experiences, termed the Hard Problem of Consciousness. Consciousness is made available as the abstract product of self-referent realization of information by strange loops through the levels of processing of the brain. Readers are introduced to the concept of the Hard Problem of Consciousness and related concepts followed by a critical discourse of different theories of consciousness. Next, the author identifies the fundamental flaw of the Integrated Information Theory (IIT) and proposes an alternative that avoids the cryptic intelligent design and panpsychism of the IIT. This author also demonstrates how something can be created out of nothing without resorting to quantum theory, while pointing out neurobiological alternatives to the bottom-up approach of quantum theories of consciousness. The book then delves into the philosophy of qualia in different physiological knowledge networks (spatial, temporal and olfactory, cortical signals, for example) to explain an action-based model consistent with the generational principles of Predictive Coding, which maps prediction and predictive-error signals for perceptual representations supporting integrated goal-directed behaviors. Conscious experiences are considered the outcome of abstractions realized out of map overlays and provided by sustained oscillatory activity. The key feature of this blueprint is that it offers a perspective of the Hard Problem of Consciousness from the point of view of the subject; the experience of ‘being the subject’ is predicted to be the realization of inference inversely mapped out of hidden causes of global integrated actions. The author explains the consistencies of his blueprint with ideas of the Global Neuronal Workspace and the Adaptive Resonance Theory of consciousness as well as with the empirical evidence supporting the Integrated Information Theory. A Blueprint for the Hard Problem of Consciousness offers a unique perspective to readers interested in the scientific philosophy and cognitive neuroscience theory in relation to models of the theory of consciousness.