Introduction to the Senses : From Biology to Computer Science.

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Cover; Introduction to the Senses: From Biology to Computer Science; Title; Copyright; Contents; Foreword; Acknowledgements; 1: Introduction and overview; 1.1 Structure of the book; 1.1.1 General concepts; 1.1.2 Mathematical preliminaries; 1.1.3 Hearing; 1.1.4 Vision; 1.1.5 Spatial information and the correspondence principle; 1.1.6 Colour and texture; 1.1.7 Touch: the somatosensory system; 1.1.8 Chemical senses; 1.1.9 Non-human senses; 1.1.10 Integration; 2: Understanding sensory systems; 2.1 Introduction; 2.2 Sensory diversity; 2.2.1 Integration and lifestyle.

2.3 Sensory information processing2.3.1 The information theory paradigm; 2.3.2 Streaming; 2.3.3 Theory of computation; 2.3.4 Mindsets and top-down processing; 2.4 Pattern recognition and computation by neurons; 2.4.1 Neurons; 2.4.2 Receptive fields and inhibition; 2.4.3 Network properties of the brain; 2.4.4 Self-masking; 2.4.5 Associative memory; 2.5 A tour of the brain; 2.5.1 A thumbnail sketch; 2.5.2 Nomenclature; 2.6 Investigative methods of brain science; 2.6.1 Anatomical methods; 2.6.2 Dynamic imaging, stimulation and blocking; 2.6.3 The impact of genetics.

2.7 Senses in computer games and virtual worlds3: Introduction to Fourier theory; 3.1 Overview; 3.1.1 Superposition, frequency and filters; 3.2 Linear systems and operators; 3.2.1 Discrete and Continuous Systems; 3.2.2 Illustrative examples of operators; 3.2.3 Vector spaces and eigenfunctions; 3.2.4 Linear shift-invariant systems; 3.3 Important functions; 3.3.1 The Kronecker delta function; 3.3.2 The Dirac delta function; 3.3.3 The impulse function; 3.3.4 The comb function; 3.3.5 Rectangular, cylinder and sinc functions; 3.4 Convolution; 3.5 Fourier Analysis and the Fourier Transform.

3.5.1 Useful properties3.5.2 Fourier Transform of common functions; 3.5.3 The optical and modulation transfer functions; 3.5.4 Autocorrelation and power spectra; 3.6 The sampling theorem; 3.6.1 Reconstruction of the signal; 3.6.2 Two-dimensional sampling; 3.7 The discrete Fourier Transform; 3.7.1 The zero frequency term; 3.7.2 Phase shifts; 3.7.3 Negative frequencies; 3.7.4 Real signals; 3.7.5 Real Fourier Transform; 3.8 Other frequency related representations; 3.8.1 Gabor functions; 3.8.2 Wavelets; 3.8.3 Finiteness; 3.9 Statistical methods for finding representations.

3.9.1 Principal component analysis3.9.2 Independent component analysis; 3.9.3 Measuring deviation from Gaussian; 4 Introduction to information theory; 4.1 Overview; 4.2 Entropy and information; 4.2.1 Information; 4.2.2 Mutual information; 4.2.3 Alphabets and codes; 4.2.4 The state transition matrix; 4.2.5 Predictive coding; 4.2.6 Huffman codes; 4.2.7 Dictionary methods; 4.2.8 Entropy in continuous systems; 4.2.9 Entropy of multivariant Gaussian; 4.2.10 Negentropy; 4.3 Algorithmic information; 4.3.1 Features and sparse coding; 4.4 Noise; 4.4.1 Photon noise; 4.4.2 Hearing and touch.

Using information theory as a unifying framework, this is a wide-ranging survey of sensory systems, covering all known senses.

Includes bibliographical references (pages 309-337) and index.

English.

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