This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information.

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These delicious and nutritious pages are packed with bits of bite-sized goodness swiped from the bleeding edge of brain science (including the reason why reading these words is changing your hippocampus at this very moment!) Shelved alongside these succulent neurological nuggets are challenging puzzles and paradoxes, eye-opening perception tests and hacks, fiendish personality quizzes and genius testers, and a grab bag of recurring treats including Eye Hacks, Algebraic Eight Ball, iDread, Wild Kingdom, and Logic of Illogic.

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