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After DigitalComputation as Done by Brains and Machines$
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James A. Anderson

Print publication date: 2017

Print ISBN-13: 9780199357789

Published to Oxford Scholarship Online: February 2018

DOI: 10.1093/acprof:oso/9780199357789.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 27 November 2021

Brain Theory

Brain Theory


(p.194) Chapter 12 Brain Theory
After Digital

James A. Anderson

Oxford University Press

What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learning, defeated the world’s best Go player. The climax of this chapter is a fictional example starring Sherlock Holmes demonstrating that complex associative computation in practice has less in common with accurate pattern recognition and more with abstract high-level conceptual inference.

Keywords:   neural networks, Hebb, Rosenblatt, AlphaGo, deep learning, Sherlock Holmes

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