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Biological and Quantum Computing for Human Vision

Book Description

Many-body interactions have been successfully described through models based on classical or quantum physics. More recently, some of the models have been related to cognitive science by researchers who are interested in describing brain activity through the use of artificial neural networks (ANNs). Biological and Quantum Computing for Human Vision: Holonomic Models and Applications presents an integrated model of human image processing up to conscious visual experience, based mainly on the Holonomic Brain Theory by Karl Pribram. This work researches possibilities for complementing neural models of early vision with the new preliminary quantum models of consciousness in order to construct a model of human image processing.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page [Chapter Title]
  4. Foreword
  5. Preface
  6. Acknowledgment
  7. Section 1:
    1. Chapter 1: Introduction to Holonomic-Compatible Models for Vision
      1. 1.1 STARTING REMARKS
      2. 1.2 THE HOLONOMIC MODEL OF VISUAL PERCEPTION
      3. 1.3 SEARCH FOR QUANTUM NEURAL SUBSTRATES OF VISION AND THEIR MODELS
      4. 1.4 QUANTUM ASSOCIATIVE NETWORK
      5. 1.5 TRYING TO INTEGRATE THE MODELS
    2. Chapter 2: Holonomic Brain Processes
      1. 2.1 GENERAL SCOPE OF THE HOLONOMIC BRAIN THEORY
      2. 2.2 MODELER’S DISCUSSION
      3. 2.3 GLOBAL VIEW: FOURIER AND GABOR TRANSFORMS; “INFOMAX ATTRACTORS”
      4. 2.4 HOLONOMIC THEORY OF VISION
    3. Chapter 3: Computational Information-Maximization Models
      1. 3.1 MAXIMAL PRESERVATION OF INFORMATION (“INFOMAX”)
      2. 3.2 I.C.A. BY BELL AND SEJNOWSKI
      3. 3.3 SPARSENESS MAXIMIZATION BY OLSHAUSEN AND FIELD
      4. 3.4 SPATIO-TEMPORAL AND EXTENDED INFOMAX
      5. 3.5 SEARCH FOR BIOLOGICAL SUPPORT OF INFOMAX MODELS
      6. 3.6 DENDRITIC FIELD COMPUTING
      7. 3.7 FROM INFOMAX-BASED IMAGE PROCESSING TO SECONDARY ASSOCIATIVE PROCESSING WITH ATTRACTOR NETS
    4. Chapter 4: Images, Associations and Conscious Experience
      1. 4.1 CONVOLUTION, CORRELATION, AND MATRIX-PROCESSING
      2. 4.2 ATTEMPT OF AN INTEGRATED MODEL OF IMAGE PROCESSING IN V1, AND BEYOND
      3. 4.3 ADDITION OF CONSCIOUS EXPERIENCE AND QUANTUM PROCESSES INTO CONSIDERATION
      4. 4.4 NON-MATHEMATICAL DESCRIPTION OF Q.A.N. MODEL
      5. 4.5 HOLOGRAPHIC PERCEPTUAL OUT-TO-SPACE BACK-PROJECTION AND OBJECT—IMAGE MATCH
      6. 4.6 DENDRITIC HOLOGRAPHY-LIKE IMAGE PROCESSING
      7. 4.7 MICROTUBULES, COHERENT SUBCELLULAR AND QUANTUM PROCESSES, AND CONSCIOUSNESS
      8. 4.8 CONSCIOUS EXPERIENCE
      9. 4.9 VISUAL CONSCIOUS EXPERIENCE
    5. Chapter 5: Computer Simulations and Applications of Quantum Associative Network
      1. 5.1 CENTRAL QUANTUM HOLOGRAPHIC MODEL
      2. 5.2 FROM RECOGNIZING IMAGES TO RECOGNIZING OBJECTS
      3. 5.3 ORTHOGONALIZATION PREPROCESSING
      4. 5.4 BIO-COMPUTATIONAL MODEL OF OBJECT RECOGNITION
      5. 5.5 QUANTUM HEBBIAN PROCESSING WITH NEURALLY SHAPED GABOR WAVELETS
      6. 5.6 SIMULATED BIO-HOLOGRAPHY WITH GABOR WAVELETS ENCODING
  8. Section 2:
    1. Chapter 6: Visual Processing As Described By Contemporary Main-Stream Neuroscience
      1. 6.1 RETINA
      2. 6.2 VISUAL PATHWAY(S); L.G.N. AND MAGNO-PARVO-BRANCHES
      3. 6.3 TO STRIATE CORTEX AND BEYOND
      4. 6.4 HIGHER VISUAL AREAS
      5. 6.5 GENERAL NEOCORTICAL ARCHITECTURE AND CONNECTIONS
      6. 6.6 SELECTIVE VISUAL ATTENTION
      7. 6.7 ELEMENTS OF ACTIVE VISUAL PERCEPTION
      8. 6.8 VISUAL MEMORY
      9. 6.9 ORIGIN OF SELECTIVITY FOR VISUAL FEATURES: A MODEL
      10. 6.10 GLOBAL RETINOTOPIC MAPPING
      11. 6.11 THE ROLE OF COLOR-INFORMATION IN EXTRACTION OF EDGES AND CONTOURS
      12. 6.12 DILEMMAS (MAIN-STREAM MODELS VS. HOLONOMIC THEORY)
    2. Chapter 7: Comparison of the Mathematical Formalism of Associative ANN and Quantum Theory
      1. 7.1 MAIN NEURO-QUANTUM ANALOGIES AND THEIR INFORMATIONAL SIGNIFICANCE
      2. 7.2 DISCREPANCIES IN PRESENTED ANALOGIES AND BEYOND
      3. 7.3 ADDITIONAL ANALOGIES
      4. 7.4 CONSEQUENCES OF NEURO-QUANTUM PARALLELS
    3. Chapter 8: Derivation of Quantum Associative Network from Hopfield-Like ANN and HNeT
      1. 8.1 CONNECTIONIST SIMULATIONS APPLIED FOR QUANTUM DYNAMICS
      2. 8.2 ASSOCIATIVE NEURAL NETWORKS
      3. 8.3 HOLOGRAPHIC NEURAL TECHNOLOGY (HNET)
      4. 8.4 QUANTUM ASSOCIATIVE NETWORK
      5. 8.5 EXAMPLE OF SIMULATED PATTERN RECOGNITION
    4. Chapter 9: Quantum Neural Information Processing
    5. Chapter 10: Quantum Phase-Hebbian Image Processing
    6. Chapter 11: Computational Models Relevant For Visual Cortex
      1. ANN WITH INHIBITORY FEEDBACK-LOOP GIVING INFOMAX OUTPUTS
      2. NETWORK OF UNITS WITH COUPLED OSCILLATORY ACTIVITIES, EMBEDDED IN NEUROPIL
      3. FIGURE/GROUND SEGMENTATION BY A NETWORK OF PHASE-COUPLED OSCILLATORS
    7. Chapter 12: APPENDIX A
    8. Chapter 13: Appendix B
      1. INTRODUCTION TO PERCEPTUAL MAPS WITH CARDINAL NEURONS
      2. TOPOLOGICALLY-CORRECT FEATURE MAPPING
      3. ADAPTIVE LEARNING BY ERROR MINIMIZATION
      4. VECTOR QUANTIZATION
    9. Chapter 14: Appendix C
    10. Chapter 15: Appendix D
  9. About the Contributors
  10. Conclusion:
  11. Summary
    1. IMPORTANT NOTE
    2. APPENDIX
  12. Introduction