Brain Computation as Hierarchical Abstraction


Author: Dana H. Ballard

Publisher: MIT Press

ISBN: 0262028611

Category: Computers

Page: 456

View: 4679

An argument that the complexities of brain function can be understood hierarchically, in terms of different levels of abstraction, as silicon computing is.

Brain Computation as Hierarchical Abstraction


Author: Dana Harry Ballard

Publisher: N.A

ISBN: 9780262323819

Category: Computers

Page: 440

View: 6793

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 Blueprint for the Hard Problem of Consciousness


Author: Paulo J. Negro

Publisher: Bentham Science Publishers

ISBN: 1681087677

Category: Science

Page: 251

View: 1753

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.

The Brain from Inside Out


Author: György Buzsáki MD, PhD

Publisher: Oxford University Press

ISBN: 0190905395

Category: Science

Page: 464

View: 5053

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 have become stagnant and points to new directions for understanding neural function. Building upon the success of 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.

Computing the Mind

How the Mind Really Works


Author: Shimon Edelman

Publisher: Oxford University Press

ISBN: 9780199717637

Category: Psychology

Page: 640

View: 7153

In a culmination of humanity's millennia-long quest for self knowledge, the sciences of the mind are now in a position to offer concrete, empirically validated answers to the most fundamental questions about human nature. What does it mean to be a mind? How is the mind related to the brain? How are minds shaped by their embodiment and environment? What are the principles behind cognitive functions such as perception, memory, language, thought, and consciousness? By analyzing the tasks facing any sentient being that is subject to stimulation and a pressure to act, Shimon Edelman identifies computation as the common denominator in the emerging answers to all these questions. Any system composed of elements that exchange signals with each other and occasionally with the rest of the world can be said to be engaged in computation. A brain composed of neurons is one example of a system that computes, and the computations that the neurons collectively carry out constitute the brain's mind. Edelman presents a computational account of the entire spectrum of cognitive phenomena that constitutes the mind. He begins with sentience, and uses examples from visual perception to demonstrate that it must, at its very core, be a type of computation. Throughout his account, Edelman acknowledges the human mind's biological origins. Along the way, he also demystifies traits such as creativity, language, and individual and collective consciousness, and hints at how naturally evolved minds can transcend some of their limitations by moving to computational substrates other than brains. The account that Edelman gives in this book is accessible, yet unified and rigorous, and the big picture he presents is supported by evidence ranging from neurobiology to computer science. The book should be read by anyone seeking a comprehensive and current introduction to cognitive psychology.

Creating Smart Enterprises

Leveraging Cloud, Big Data, Web, Social Media, Mobile and IoT Technologies


Author: Vivek Kale

Publisher: CRC Press

ISBN: 1351648497

Category: Business & Economics

Page: 380

View: 7875

"Vivek Kale's Creating Smart Enterprises goes smack-dab at the heart of harnessing technology for competing in today's chaotic digital era. Actually, for him, it's SMACT-dab: SMACT (Social media, Mobile, Analytics and big data, Cloud computing, and internet of Things) technologies. This book is required reading for those that want to stay relevant and win, and optional for those that don't." —Peter Fingar, Author of Cognitive Computing and business technology consultant Creating Smart Enterprises unravels the mystery of social media, mobile, analytics and big data, cloud, and Internet of Things (SMACT) computing and explains how it can transform the operating context of business enterprises. It provides a clear understanding of what SMACT really means, what it can do for smart enterprises, and application areas where it is practical to use them. All IT professionals who are involved with any aspect of a SMACT computing project will profit by using this book as a roadmap to make a more meaningful contribution to the success of their computing initiatives. This pragmatic book: Introduces the VUCA (volatility, uncertainty, complexity, and ambiguity) business ecosystem confronted by the businesses today. Describes the challenges of defining business and IT strategies and of aligning them as well as their impact on enterprise governance. Provides a very wide treatment of the various components of SMACT computing, including the Internet of Things (IoT) and its constituting technologies like RFID, wireless networks, sensors, and wireless sensor networks (WSNs). This book addresses the key differentiator of SMACT computing environments and solutions that combine the power of an elastic infrastructure with analytics. The SMACT environment is cloud-based and inherently mobile. Information management processes can analyze and discern recurring patterns in colossal pools of operational and transactional data. Analytics, big data, and IoT computing leverage and transform these data patterns to help create successful, smart enterprises.

Biological Neural Networks: Hierarchical Concept of Brain Function


Author: Konstantin V. Baev

Publisher: Springer Science & Business Media

ISBN: 9780817638597

Category: Medical

Page: 273

View: 5150

This book is devoted to a novel conceptual theoretical framework of neuro science and is an attempt to show that we can postulate a very small number of assumptions and utilize their heuristics to explain a very large spectrum of brain phenomena. The major assumption made in this book is that inborn and acquired neural automatisms are generated according to the same func tional principles. Accordingly, the principles that have been revealed experi mentally to govern inborn motor automatisms, such as locomotion and scratching, are used to elucidate the nature of acquired or learned automat isms. This approach allowed me to apply the language of control theory to describe functions of biological neural networks. You, the reader, can judge the logic of the conclusions regarding brain phenomena that the book derives from these assumptions. If you find the argument flawless, one can call it common sense and consider that to be the best praise for a chain of logical conclusions. For the sake of clarity, I have attempted to make this monograph as readable as possible. Special attention has been given to describing some of the concepts of optimal control theory in such a way that it will be under standable to a biologist or physician. I have also included plenty of illustra tive examples and references designed to demonstrate the appropriateness and applicability of these conceptual theoretical notions for the neurosciences.

Spatial Information Theory. Cognitive and Computational Foundations of Geographic Information Science

International Conference COSIT'99 Stade, Germany, August 25-29, 1999 Proceedings


Author: C. Freksa

Publisher: Springer Science & Business Media

ISBN: 3540663657

Category: Computers

Page: 476

View: 5283

This book constitutes the refereed proceedings of the International Conference on Spatial Information Theory, COSIT '99, held in Stade, Germany, in August 1999. The 30 revised full papers presented were carefully reviewed and selected from 70 submissions. The book is divided into topical sections on landmarks and navigation, route directions, abstraction and spatial hierarchies, spatial reasoning calculi, ontology of space, visual representation and reasoning, maps and routes, and granularity and qualitative abstraction.

Evolution and the Emergent Self

The Rise of Complexity and Behavioral Versatility in Nature


Author: Raymond L. Neubauer

Publisher: Columbia University Press

ISBN: 0231521685

Category: Science

Page: 320

View: 4874

Evolution and the Emergent Self is an eloquent and evocative new synthesis that explores how the human species emerged from the cosmic dust. Lucidly presenting ideas about the rise of complexity in our genetic, neuronal, ecological, and ultimately cosmological settings, the author takes readers on a provocative tour of modern science's quest to understand our place in nature and in our universe. Readers fascinated with "Big History" and drawn to examine big ideas will be challenged and enthralled by Raymond L. Neubauer's ambitious narrative. How did humans emerge from the cosmos and the pre-biotic Earth, and what mechanisms of biological, chemical, and physical sciences drove this increasingly complex process? Neubauer presents a view of nature that describes the rising complexity of life in terms of increasing information content, first in genes and then in brains. The evolution of the nervous system expanded the capacity of organisms to store information, making learning possible. In key chapters, the author portrays four species with high brain:body ratios—chimpanzees, elephants, ravens, and dolphins—showing how each species shares with humans the capacity for complex communication, elaborate social relationships, flexible behavior, tool use, and powers of abstraction. A large brain can have a hierarchical arrangement of circuits that facilitates higher levels of abstraction. Neubauer describes this constellation of qualities as an emergent self, arguing that self-awareness is nascent in several species besides humans and that potential human characteristics are embedded in the evolutionary process and have emerged repeatedly in a variety of lineages on our planet. He ultimately demonstrates that human culture is not a unique offshoot of a language-specialized primate, but an analogue of fundamental mechanisms that organisms have used since the beginning of life on Earth to gather and process information in order to buffer themselves from fluctuations in the environment. Neubauer also views these developments in a cosmic setting, detailing open thermodynamic systems that grow more complex as the energy flowing through them increases. Similar processes of increasing complexity can be found in the "self-organizing" structures of both living and nonliving forms. Recent evidence from astronomy indicates that planet formation may be nearly as frequent as star formation. Since life makes use of the elements commonly seeded into space by burning and expiring stars, it is reasonable to speculate that the evolution of life and intelligence that happened on our planet may be found across the universe.

Hierarchical Neural Networks for Image Interpretation


Author: Sven Behnke

Publisher: Springer Science & Business Media

ISBN: 3540407227

Category: Computers

Page: 224

View: 1134

Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.