Synopsis
These volumes explore recent research in neural networks that has advanced our understanding of human and machine perception. Contributions from international researchers address both theoretical and practical issues related to the feasibility of neural network models explaining human perception and implementing machine perception. Volume 1 covers models for understanding human perception in terms of distributed computation as well as examples of neural network models for machine perception. Volume 2 examines computational and adaptational problems related to the use of neural systems and discusses the corresponding hardware architectures needed to implement neural networks for perception.
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Neural Networks for Perception: Computation, Learning, and Architecture : Vol. 2
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Neural Networks for Perception: Computation, Learning, and Architecture
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