How to Build a Brain: A Neural Architecture for Biological Cognition (Oxford Series on Cognitive Models and Architectures), by Chris Eliasmith
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How to Build a Brain: A Neural Architecture for Biological Cognition (Oxford Series on Cognitive Models and Architectures), by Chris Eliasmith
Ebook PDF Online How to Build a Brain: A Neural Architecture for Biological Cognition (Oxford Series on Cognitive Models and Architectures), by Chris Eliasmith
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), by Chris Eliasmith - Amazon Sales Rank: #75448 in Books
- Published on: 2015-06-01
- Original language: English
- Number of items: 1
- Dimensions: 6.90" h x 1.00" w x 10.00" l, .0 pounds
- Binding: Paperback
- 480 pages
How to Build a Brain: A Neural Architecture for Biological Cognition (Oxford Series on Cognitive Models and Architectures), by Chris Eliasmith Review "How to Build a Brain takes on a daunting task, focusing on those parts that we think are important for memory, attention, and planning. Previous attempts at building a cognitive architecture have used symbols or connectionist networks, but Eliasmith uses spiking neurons and models specific brain regions. Categories and semantics emerge from the architecture. The way that all these moving parts work together provides insights into both the nature of cognition and brain function." --Terrence Sejnowski, Professor and Laboratory Head, Computational Neurobiology Laboratory, Howard Hughes Medical Institute Investigator, Francis Crick Chair, Salk Institute"Eliasmith offers a unified theory of cognition that rests on the mechanism of a semantic pointer, namely, a compressed neural representation that can stand as a symbol for a more detailed semantic state or be decompressed to reproduce it, in compositional cognitive processes. Ambitious state-of-the-art modeling grounds the semantic pointer architecture in populations of spiking neurons, providing concrete neural accounts of high-level processes, including attention, learning, memory, syntax, semantics, and reasoning. Along with offering a powerful new approach for integrating cognition and neuroscience, Eliasmith provides detailed technical accounts of his system, with accompanying software that will serve both students and fellow modelers well." --Lawrence W. Barsalou, Professor, Department of Psychology, Emory University
About the Author Chris Eliasmith is Canada Research Chair in Theoretical Neuroscience at the University of Waterloo.
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25 of 25 people found the following review helpful. Hands on Neural Sims-- A Roboticists View By Let's Compare Options Preptorial Here's the "real life," most robust and up to date neural cognition text available today, to add supplemental hard science to the numerous softer and more contemplative (less practically sim ready) "Kurzweil" like models, eg: (How to Create a Mind: The Secret of Human Thought Revealed.The book doubles as an "owners manual" for the author's Nengo neuro sim program (meaning Neural ENgineering Objects, not the Japanese era). Videos are available on how the book and system integrate-- check out section 1.5 in Amazon's generous preview peek for the website address. While you're at it, there is a LOT of detail given in this worthwhile preview that will immediately help you determine if this is the book for you.I design domain specific languages for robotics and am always interested in new and different views of neural vs. semiconductor cognitive sims. Unfortunately to be honest if not crass, most of the books out there today are big on speculation and light on anything you can model. That's where this book shines from my narrow frame-- you can build your own versions of the authors premises and "try" them on what has become the biggest, most active and accessed "online brain sim" on the planet!Do you have an interest in sims of semantics or perception? My slice is more on "translation" from the compiler side, but this book is wonderful in its coverage of practical aspects of semantics, perception, memory, what the authors call "semantic pointers" and of course the must mention topics today of plasticity and fluid intelligence.I'll wait for a Neurological researcher to opine on this from the biological side, but for pure "translational" delight between brain and robotic mechanisms, this book can't be beat. It really is the only up to date text on the topic, as extensive brain sims are mostly covered in journals. You should know that the majority of the book IS meant to present a new and coherent theory of brain architecture, but the amount of detail, history, where we have been and where we are going the author covers is worth the price of the book itself, and of course is architecture independent.As an EE I love math, but if you're math adverse, don't dismiss this, as the author has relegated the most complex math to the appendices. (You can't read neuro without running into differential equations due to the importance of dynamical systems in the models. But from an EE viewpoint, asp and dsp also involve time vs. frequency and we use similar Fourier etc. methods for different reasons on the robotics side).Very readable, enlightening, and frankly fun, since you really can get hands on with the sims. Highly recommended, but suggest you take advantage of the Amazon preview before investing. Hats off to authors and publishers who have enough pride and confidence in the value of their work to let us have a significant preview like this.Emailer answer: "What does Eliasmith think of Newell?" A. He mentions him throughout, with respect, but with the objective of bringing Newell's architectures closer to biological underpinnings. SOAR and other production systems are covered, but Chris insists that bringing in dynamic systems is crucial. The example, from my field, is that roboticists use dynamics (PDEs, signal processing, stats) instead of the embedded if-thens of production systems, due to both the "need for speed" in dynamics as well as flexibility. This has the additional benefit of suggesting a more robust model that includes both biology and algorithmic models, and Eliasmith invites both computational Neurology AND psych to converge in testing his new architecture. Emailer's Reference: Unified Theories of Cognition (William James Lectures).DO take advantage of the author's/ Amazon's wonderful look inside feature, Dr. Chris even gives an extensive history in that look of what led up to this new model. One interesting gap I found in the otherwise extensive bib (nearly 30 pages) is Bach. You might find his book/dissertation interesting in summary and contrast to many of Chris' unique perspectives: Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition (Oxford Series on Cognitive Models and Architectures). Both of course mention Newell extensively.As a completely unscientific, but fun exercise, whenever I evaluate a title on cognitive architecture, I immediately go to the index and look for the seminal (at least in programming/robotics) word: "recursion." Newell and Chris don't index it specifically, Bach has half a dozen entries and hundreds cross referencing hierarchy and reflection, similar concepts, as do Chris and Newell. If you like or are curious about this concept, ubiquitous in this field but not always re-cognized, you'll greatly enjoy Corballis: The Recursive Mind: The Origins of Human Language, Thought, and Civilization. In fact, mathematically, there are recent ("matrix like") theories extending recursion to the mathematics beneath the entire universe, and from a physicist's frame, Dr. Tegmark is one of the first to explore it extensively: Our Mathematical Universe: My Quest for the Ultimate Nature of Reality.
5 of 5 people found the following review helpful. Adventures in Brain Land. By Simon Laub When most of us say that we want to to know ''how the brain works'',Chris Eliasmith suspect that we don't just want to know how a single neuron works, or what brain centers have increased activity when we (e.g.) read, or what chemicals are in abundance when people are depressed.No, this is all part of the answer, but we want more!We want to know what goes on in the mind, starting from perception to and all the way to an action. In some detail, but also with an overview. We want to "really" understand.Obviously, this is a tall order. But as understanding human cognition is understanding who we are - we will proceed anyway...And here Chris Eliasmith's book is an excellent guide and helper for this, our most important journey.Many books out there are big on speculation and light on anything you can model.But seriously, if you can't model it - somehow - can you then be said to understand it?We need the ''wheels'' and ''gears'' of cognition, and we need modelling at an ''appropriate'' level.We don't want to model each neuron with 100 equations or so, if it is not really necessary.We just want to capture the essential features.In order to bridge the gap between the neural level and the level of human-like cognition, and come up with this "appropriate" level for the models, the book introduces us to the concept of semantic pointers. In my understanding, this is a kind of symbol, a compressed version of the information contained in a more complex representation.And this is where things begin to become interesting.As I understand Chris Eliasmith, semantic processing in the brain might start a kind of simulation. And in that way be central to how we think.Certainly, simulation is crucial. Chris Eliasmith writes that"When people are asked to think and reason about objects (such as a watermelon), they do not merely activate words that are associated with watermelons, but seem to implicitly activate representations that are typical of watermelon backgrounds, bring up emotional associations with watermelons, and activate tactile, auditory, and visual representations of water melons".It follows that good brain models should somehow capture such mechanisms.Take home message: Semantic processing and simulations will be important in the coming phases of understanding cognition and building cognitive models.And, playing around with the models presented in this book seems to be an important first step in that direction.Still, are the models in the book "good enough"?Well, they can take image sequences as input and produce motor behaviour as output.The scale is somewhat "modest" (2.5 million neurons) compared toother models, but in terms of what it can do, it is currently the largest functional simulation of a brain, demonstrating recognition, classification, motor behaviours etc.So, Chris Eliasmith is certainly correct in stating that it has something to offer to the current state-of-the-art.Indeed, a very impressive number of brain centers are modelled with (the books) Semantic Pointer Architecture Unified Network (Spaun).Obviously, the whole brain is not modelled yet, and equally obviously, it is still only a simplified model (good or bad) of what might be going on in a brain.Afterall, building a brain is a difficult thing to do....Still, there is good input in the book about models for visual processing (modelling what might be going on in the brains V1,V2,V4 an IT areas), action selection (modelling the Basal Ganglia) and motor processing (modelling the primary motor cortex) and much, much more.Still, there are many unsolved problems in this land of modelling.For example, when we assume that an object has a backside even though we cannot see it, we are often said to be relying on mechanisms of perceptual inference.Current models do not have a unified theory of inference, but, remarkably, Eliasmith thinks SPA can help us understand inference by characterizing it in the context of a functional architecture. This is really encouraging.Again, the scope IS very impressive.And so is Eliasmiths intellectual honesty when he writes that "emotion and stimulus valuation are largely absent (in the model). These play a cricial role in guiding behavior and need to be integrated into the SPA".This is where it all gets very interesting, a complete model should obviously have a lot about feelings - Feelings are afterall what makes us "own" our thoughts, our own mind, and they are the background to all of "the cognitive calculations" that we have been introduced to in the book.So, it is disappointing (for this reader) that there is so little about feelings.In other (less comprehensive) books the omission wouldn't have been felt so hard, but given the amount of material covered here it does feel like a let-down.Why? Is it because we currently only have neural correlates of say "pain", but no one who has offered us a convincing explanation of how neurons can make a "calculation" that we experience as "pain". If so, then I certainly would have liked to hear Chris Eliasmiths speculations on this. And how we could possibly proceed here.Anyway, the brain is a big thing, and including the whole thing in one book is probably somewhat optimistic...And it is certainly a sign of a good book that you want more, not less.A great read.-Simon
3 of 3 people found the following review helpful. the book bridges detailed description of neural ensembles and large scale modeling of human behaviour By Bernd J. Kröger I like this book very much, because it gives a very intuitive approach to neural scince as well as to large scale brain models. It is possible to read this book without a deep mathematical background. An interesting feature is that your can work on examples at the end of each chapter which are implemented in NENGO.
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How to Build a Brain: A Neural Architecture for Biological Cognition (Oxford Series on Cognitive Models and Architectures), by Chris Eliasmith
