Brain Computation as Hierarchical Abstraction

Brain Computation as Hierarchical Abstraction PDF

Author: Dana H. Ballard

Publisher: MIT Press

Published: 2015-02-20

Total Pages: 457

ISBN-13: 0262323826

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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.

Brain Computation as Hierarchical Abstraction

Brain Computation as Hierarchical Abstraction PDF

Author: Dana Harry Ballard

Publisher:

Published: 2015-02-20

Total Pages: 440

ISBN-13: 9780262323819

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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.

Brain Computation as Hierarchical Abstraction

Brain Computation as Hierarchical Abstraction PDF

Author: Dana H. Ballard

Publisher: MIT Press

Published: 2015-02-20

Total Pages: 457

ISBN-13: 0262028611

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

An Introduction to Natural Computation

An Introduction to Natural Computation PDF

Author: Dana H. Ballard

Publisher: MIT Press

Published: 1999-01-22

Total Pages: 338

ISBN-13: 9780262522588

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This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brains programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It stresses the broad spectrum of learning models—ranging from neural network learning through reinforcement learning to genetic learning—and situates the various models in their appropriate neural context. To write about models of the brain before the brain is fully understood is a delicate matter. Very detailed models of the neural circuitry risk losing track of the task the brain is trying to solve. At the other extreme, models that represent cognitive constructs can be so abstract that they lose all relationship to neurobiology. An Introduction to Natural Computation takes the middle ground and stresses the computational task while staying near the neurobiology.

An Introductory Course in Computational Neuroscience

An Introductory Course in Computational Neuroscience PDF

Author: Paul Miller

Publisher: MIT Press

Published: 2018-10-02

Total Pages: 405

ISBN-13: 0262038250

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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.

After Digital

After Digital PDF

Author: James A. Anderson

Publisher: Oxford University Press

Published: 2017

Total Pages: 401

ISBN-13: 0199357781

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After Digital looks at where the field of computation began and where it might be headed, and offers predictions about a collaborative future relationship between human cognition and mechanical computation.

Computing the Mind

Computing the Mind PDF

Author: Shimon Edelman

Publisher: Oxford University Press

Published: 2008-09-08

Total Pages: 640

ISBN-13: 9780199717637

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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.

The Brain Abstracted

The Brain Abstracted PDF

Author: M. Chirimuuta

Publisher: MIT Press

Published: 2024-03-05

Total Pages: 377

ISBN-13: 0262548046

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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.

Mind Mapping and Artificial Intelligence

Mind Mapping and Artificial Intelligence PDF

Author: Jose Maria Guerrero

Publisher: Academic Press

Published: 2022-10-22

Total Pages: 254

ISBN-13: 0128202424

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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

Modeling Neural Circuits Made Simple with Python

Modeling Neural Circuits Made Simple with Python PDF

Author: Robert Rosenbaum

Publisher: MIT Press

Published: 2024-03-19

Total Pages: 169

ISBN-13: 0262548089

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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