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      Explicit Logic Circuits Predict Local Properties of the Neocortex's Physiology and Anatomy

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      PLoS ONE
      Public Library of Science

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          Abstract

          Background

          Two previous articles proposed an explicit model of how the brain processes information by its organization of synaptic connections. The family of logic circuits was shown to generate neural correlates of complex psychophysical phenomena in different sensory systems.

          Methodology/Principal Findings

          Here it is shown that the most cost-effective architectures for these networks produce correlates of electrophysiological brain phenomena and predict major aspects of the anatomical structure and physiological organization of the neocortex. The logic circuits are markedly efficient in several respects and provide the foundation for all of the brain's combinational processing of information.

          Conclusions/Significance

          At the local level, these networks account for much of the physical structure of the neocortex as well its organization of synaptic connections. Electronic implementations of the logic circuits may be more efficient than current electronic logic arrays in generating both Boolean and fuzzy logic.

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          Most cited references1

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          Explicit Logic Circuits Discriminate Neural States

          Lane Yoder (2009)
          The magnitude and apparent complexity of the brain's connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron's response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems.
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            Author and article information

            Contributors
            Role: Editor
            Journal
            PLoS One
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2010
            16 February 2010
            : 5
            : 2
            : e9227
            Affiliations
            [1]Department of Mathematics, University of Hawaii, Kapiolani, Honolulu, Hawaii, United States of America
            Newcastle University, United Kingdom
            Author notes

            Wrote the paper: LY.

            Article
            09-PONE-RA-10255R2-A
            10.1371/journal.pone.0009227
            2821925
            20169077
            9dec8f37-0ab6-4d8f-af5f-9fec5501781f
            Lane Yoder. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            History
            : 11 May 2009
            : 20 January 2010
            Page count
            Pages: 16
            Categories
            Research Article
            Biophysics/Cell Signaling and Trafficking Structures
            Biophysics/Theory and Simulation
            Biotechnology/Bioengineering
            Computational Biology/Computational Neuroscience
            Computational Biology/Signaling Networks
            Computer Science/Hardware
            Neuroscience/Neuronal Signaling Mechanisms
            Neuroscience/Theoretical Neuroscience
            Computer Science/Natural and Synthetic Vision
            Neuroscience/Natural and Synthetic Vision

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            Uncategorized

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