One goal of researchers in neuroscience, psychology, and artificial intelligence is to build theoretical models that can explain the flexibility and adaptiveness of biological systems. How to Build a Brain provides a guided exploration of a new cognitive architecture that takes biological detail seriously while addressing cognitive phenomena. The Semantic Pointer Architecture (SPA) introduced in this book provides a set of tools for constructing a wide range of biologically constrained perceptual, cognitive, and motor models. Examples of such models are provided to explain a wide range of data including single-cell recordings, neural population activity, reaction times, error rates, choice behavior, and fMRI signals. Each of the models addressed in the book introduces a major feature of biological cognition, including semantics, syntax, control, learning, and memory. These models are presented as integrated considerations of brain function, giving rise to what is currently the world's largest functional brain model. The book also compares the Semantic Pointer Architecture with the current state of the art, addressing issues of theory construction in the behavioral sciences, semantic compositionality, and scalability, among other considerations. The book concludes with a discussion of conceptual challenges raised by this architecture, and identifies several outstanding challenges for SPA and other cognitive architectures. Along the way, the book considers neural coding, concept representation, neural dynamics, working memory, neuroanatomy, reinforcement learning, and spike-timing dependent plasticity. Eight detailed, hands-on tutorials exploiting the free Nengo neural simulation environment are also included, providing practical experience with the concepts and models presented throughout.

How to Build a Brain: A Neural Architecture for Biological Cognition (Oxford Series on Cognitive Models and Architectures)

Science & Math

Eliasmith, Chris

Behavioral Sciences

Oxford University Press

One goal of researchers in neuroscience, psychology, and artificial intelligence is to build theoretical models that are able to explain the flexibility and adaptiveness of biological systems. How to build a brain provides a detailed guided exploration of a new cognitive architecture that takes biological detail seriously, while addressing cognitive phenomena. The Semantic Pointer Architecture (SPA) introduced in this book provides a set of tools for constructing a wide range of biologically constrained perceptual, cognitive, and motor models. Examples of such models are provided, and they are shown to explain a wide range of data including single cell recordings, neural population activity, reaction times, error rates, choice behavior, and fMRI signals. Each of these models introduces a major feature of biological cognition addressed in the book, including semantics, syntax, control, learning, and memory. These models are not introduced as independent considerations of brain function, but instead integrated to give rise to what is currently the world's largest functional brain model. The last half of this book compares the Semantic Pointer Architecture with the current state-of-the-art, addressing issues of theory construction in the behavioral sciences, semantic compositionality, and scalability, among other considerations. The book concludes with a discussion of conceptual challenges raised by this architecture, and identifies several outstanding challenges for this, and other, cognitive architectures. Along the way, the book considers neural coding, concept representation, neural dynamics, working memory, neuroanatomy, reinforcement learning, and spike-timing dependent plasticity. The book includes 8 detailed, hands-on tutorials exploiting the free Nengo neural simulation environment, providing practical experience with the concepts and models presented throughout.

For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain. The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.

Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems

Computers & Technology

Anderson, C. H.

Computer Science

Bradford Books

For years, researchers have used the theoretical tools of engineering to understand neural systems, but much of this work has been conducted in relative isolation. In Neural Engineering, Chris Eliasmith and Charles Anderson provide a synthesis of the disparate approaches current in computational neuroscience, incorporating ideas from neural coding, neural computation, physiology, communications theory, control theory, dynamics, and probability theory. This synthesis, they argue, enables novel theoretical and practical insights into the functioning of neural systems. Such insights are pertinent to experimental and computational neuroscientists and to engineers, physicists, and computer scientists interested in how their quantitative tools relate to the brain.
The authors present three principles of neural engineering based on the representation of signals by neural ensembles, transformations of these representations through neuronal coupling weights, and the integration of control theory and neural dynamics. Through detailed examples and in-depth discussion, they make the case that these guiding principles constitute a useful theory for generating large-scale models of neurobiological function. A software package written in MatLab for use with their methodology, as well as examples, course notes, exercises, documentation, and other material, are available on the Web.

Neural Engineering: Computation, Representation, And Dynamics In Neurobiological Systems (Computational Neuroscience series)

Politics & Social Sciences

Anderson, Charles H.

Anthropology

Neural Engineering : Computation, Representation,

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