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  1. Cortical connections and parallel processing: Structure and function.Dana H. Ballard - 1986 - Behavioral and Brain Sciences 9 (1):67-90.
    The cerebral cortex is a rich and diverse structure that is the basis of intelligent behavior. One of the deepest mysteries of the function of cortex is that neural processing times are only about one hundred times as fast as the fastest response times for complex behavior. At the very least, this would seem to indicate that the cortex does massive amounts of parallel computation.This paper explores the hypothesis that an important part of the cortex can be modeled as a (...)
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  • What's in the term connectionist?.Christof Koch - 1986 - Behavioral and Brain Sciences 9 (1):100-101.
  • Value units make the right connections.Dana H. Ballard - 1986 - Behavioral and Brain Sciences 9 (1):107-120.
    The cerebral cortex is a rich and diverse structure that is the basis of intelligent behavior. One of the deepest mysteries of the function of cortex is that neural processing times are only about one hundred times as fast as the fastest response times for complex behavior. At the very least, this would seem to indicate that the cortex does massive amounts of parallel computation.This paper explores the hypothesis that an important part of the cortex can be modeled as a (...)
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  • Auditory, Visual and Audiovisual Speech Processing Streams in Superior Temporal Sulcus.Jonathan H. Venezia, Kenneth I. Vaden, Feng Rong, Dale Maddox, Kourosh Saberi & Gregory Hickok - 2017 - Frontiers in Human Neuroscience 11.
  • Establishing motion correspondence using extended temporal scope.C. J. Veenman, M. J. T. Reinders & E. Backer - 2003 - Artificial Intelligence 145 (1-2):227-243.
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  • Connectionist computing and neural machinery: Examining the test of “timing”.John K. Tsotsos - 1986 - Behavioral and Brain Sciences 9 (1):106-107.
  • What does the cortex do?Mriganka Sur - 1986 - Behavioral and Brain Sciences 9 (1):105-105.
  • Computational neuroscience.Terrence J. Sejnowski - 1986 - Behavioral and Brain Sciences 9 (1):104-105.
  • The Shape of a Vehicle Windshield Affects Reaction Time and Brain Activity During a Target Detection Task.Takafumi Sasaoka, Maro G. Machizawa, Yoshihisa Okamoto, Koji Iwase, Toshihiro Yoshida, Nanae Michida, Atsuhide Kishi, Masaki Chiba, Kazuo Nishikawa, Shigeto Yamawaki & Takahide Nouzawa - 2020 - Frontiers in Human Neuroscience 14.
  • An active vision architecture based on iconic representations.Rajesh P. N. Rao & Dana H. Ballard - 1995 - Artificial Intelligence 78 (1-2):461-505.
  • The well-designed logical robot: Learning and experience from observations to the Situation Calculus.Fiora Pirri - 2011 - Artificial Intelligence 175 (1):378-415.
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  • Old dogmas and new axioms in brain theory.Andràs J. Pellionisz - 1986 - Behavioral and Brain Sciences 9 (1):103-104.
  • Estimation of optical flow based on higher-order spatiotemporal derivatives in interlaced and non-interlaced image sequences.Michael Otte & Hans-Hellmut Nagel - 1995 - Artificial Intelligence 78 (1-2):5-43.
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  • A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges.Yee-Hui Oh, John See, Anh Cat Le Ngo, Raphael C. -W. Phan & Vishnu M. Baskaran - 2018 - Frontiers in Psychology 9:336565.
    Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today. Although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we (...)
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  • On the estimation of optical flow: Relations between different approaches and some new results.Hans-Hellmut Nagel - 1987 - Artificial Intelligence 33 (3):299-324.
  • Two tests for the value unit model: Multicell recordings and pointers.David Mumford - 1986 - Behavioral and Brain Sciences 9 (1):102-103.
  • Dynamic functional and structural analysis of living cells: New tools for vital staining of nuclear DNA and for characterisation of cell motion.François Leitner, Sylvain Paillasson, Xavier Ronot & Jacques Demongeot - 1995 - Acta Biotheoretica 43 (4):299-317.
    Increasing interest has been paid to applications of fluorescence measurements to analyze physiological mechanisms in living cells. However, few studies have taken advantage of DNA quantification by fluorometry for dynamic assessment of chromatin organization as well as cell motion during the cell cycle. This approach involves both optimal conditions for DNA staining and cell tracking methods. In this context, this report describes a stoichiometric method for nuclear DNA specific staining, using the bisbenzimidazole dye Hoechst 33342 associated with verapamil, a calcium (...)
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  • The gap from sensation to cognition.Michael S. Landy - 1986 - Behavioral and Brain Sciences 9 (1):101-102.
  • Detection of surface orientation and motion from texture by a stereological technique.Ken-Ichi Kanatani - 1984 - Artificial Intelligence 23 (2):213-237.
  • Numerical shape from shading and occluding boundaries.Katsushi Ikeuchi & Berthold K. P. Horn - 1981 - Artificial Intelligence 17 (1-3):141-184.
  • “Determining optical flow”: a retrospective.Berthold K. P. Horn & B. G. Schunck - 1993 - Artificial Intelligence 59 (1-2):81-87.
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  • “Grandmother networks” and computational economy.J. J. Hopfield - 1986 - Behavioral and Brain Sciences 9 (1):100-100.
  • Invariant and programmable neuropsychological systems are fibrations.William C. Hoffman - 1986 - Behavioral and Brain Sciences 9 (1):99-100.
  • Computations underlying the measurement of visual motion.Ellen C. Hildreth - 1984 - Artificial Intelligence 23 (3):309-354.
  • A non-rigid registration algorithm for dynamic breast MR images.Paul M. Hayton, Michael Brady, Stephen M. Smith & Niall Moore - 1999 - Artificial Intelligence 114 (1-2):125-156.
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  • Does the brain compute?Erich Harth - 1986 - Behavioral and Brain Sciences 9 (1):98-99.
  • Brain metaphors, theories, and facts.Stephen Grossberg - 1986 - Behavioral and Brain Sciences 9 (1):97-98.
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  • Cortical architectures and value unit encoding.Charles D. Gilbert - 1986 - Behavioral and Brain Sciences 9 (1):96-97.
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  • Abstract solutions versus neurobiologically plausible problems.Jeffrey Foss - 1986 - Behavioral and Brain Sciences 9 (1):95-96.
  • A theory of spatio-temporal aggregation for vision.Bruce E. Flinchbaugh & B. Chandrasekaran - 1981 - Artificial Intelligence 17 (1-3):387-407.
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  • What's the connection?Leif H. Finkel & George N. Reeke - 1986 - Behavioral and Brain Sciences 9 (1):94-95.
  • The how and why of what went where in apparent motion: Modeling solutions to the motion correspondence problem.Michael R. Dawson - 1991 - Psychological Review 98 (4):569-603.
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  • Phase-space representation and coordinate transformation: A general paradigm for neural computation.Paul M. Churchland - 1986 - Behavioral and Brain Sciences 9 (1):93-94.
  • Artificial intelligence and robotics.Michael Brady - 1985 - Artificial Intelligence 26 (1):79-121.
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  • Unity of perception.Bruce M. Bennett, Donald D. Hoffman & Chetan Prakash - 1991 - Cognition 38 (3):295-334.
  • Retrospective on “Interpreting line drawings as three-dimensional surfaces”.Harry G. Barrow & J. M. Tenenbaum - 1993 - Artificial Intelligence 59 (1-2):71-80.
  • Connectionist value units: Some concerns.John A. Barnden - 1986 - Behavioral and Brain Sciences 9 (1):92-93.
    This paper is a commentary on the target article by Dana H. Ballard, “Cortical connections and parallel processing: Structure and function”, in the same issue of the journal, pp. 67–120. -/- I raise some issues about the connectionist or neural-network implementation of information and information processing. Issues include the sharing of information by different parts of a connectionist/neural network, the copying of complex information from one place to another in a network, the possibility of connection weights not being synaptic weights, (...)
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  • The Hierarchical Evolution in Human Vision Modeling.Dana H. Ballard & Ruohan Zhang - 2021 - Topics in Cognitive Science 13 (2):309-328.
    Ballard and Zhang offer a fascinating review of how computational models of human vision have evolved since David Marr proposed his Tri‐Level Hypothesis, with a focus on the refinement of algorithm descriptions over time.
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  • Parameter nets.Dana H. Ballard - 1984 - Artificial Intelligence 22 (3):235-267.
  • Value encoding of patterns and variable encoding of transformations?John C. Baird - 1986 - Behavioral and Brain Sciences 9 (1):91-92.
  • Value, variable, and coarse coding by posterior parietal neurons.Richard A. Andersen - 1986 - Behavioral and Brain Sciences 9 (1):90-91.
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