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Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields (Studies in Systems, Decision and Control)
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From the Back Cover
This intriguing book was born out of the many discussions the authors had in the past 10 years about the role of scale-free structure and dynamics in producing intelligent behavior in brains. The microscopic dynamics of neural networks is well described by the prevailing paradigm based in a narrow interpretation of the neuron doctrine. This book broadens the doctrine by incorporating the dynamics of neural fields, as first revealed by modeling with differential equations (K-sets). The book broadens that approach by application of random graph theory (neuropercolation). The book concludes with diverse commentaries that exemplify the wide range of mathematical/conceptual approaches to neural fields. This book is intended for researchers, postdocs, and graduate students, who see the limitations of network theory and seek a beachhead from which to embark on mesoscopic and macroscopic neurodynamics.
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Product details
Series: Studies in Systems, Decision and Control (Book 39)
Hardcover: 262 pages
Publisher: Springer; 1st ed. 2016 edition (November 3, 2015)
Language: English
ISBN-10: 3319244043
ISBN-13: 978-3319244044
Product Dimensions:
6.1 x 0.6 x 9.4 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
1 customer review
Amazon Best Sellers Rank:
#2,460,112 in Books (See Top 100 in Books)
Kozma and Freeman were pioneers in finding chaotic attractors in the olfactory system, and they pursued research for years afterwards. This book is an overview of what they learned. They also talk about K-sets, which was inspired by their work - a hierarchy of interacting inhibitory and excitatory clusters of neurons that couple oscillators together and have been used for all sorts of practical neural net applications in the AI field. My only gripe is that for a novice, you really have to read the references in some cases, or even learn whole fields (like Parallel Cellular Automata), before you understand what you are reading.
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