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Learning resources: chapter 8


Sections 8.1 - Neurally Inspired Models of Information Processing

1. Artificial neural networks
Artificial neural networks (article by Alexx Kay, 2001; from Computerworld)
What are artificial neural networks? (paper by Anders Krogh, 2008; from Nature Biotechnology, 26)
Artificial neural networks: A tutorial (paper by Anil Jain and Jianchang Mao, 1996; from Computer, 29)
Connectionism, history of (entry by Ken Aizawa; from the Dictionary of Philosophy of Mind)
Connectionism: An introduction (unit by Robert Stufflebeam; from The MindProject) 

For more links see the online resources for section 3.3.

Sections 8.2 - Single-Layer Networks and Boolean Functions

1. Boolean functions and related topics
How Boolean logic works (article by Marshal Brain; from
List of Boolean algebra topics (from Wikipedia)

2.Single-layer networks
McCulloch-Pitts neuron (applet)
McCulloch-Pitts Neurons (unit by Michael Marsalli; from The Mindproject)
PowerPoint slides on connectionism, including McCulloch and Pitts (from Jaap Murre)

3. Advanced study
Neural Networks: A Systematic Introduction (book by Raul Rojas, 1996)

Sections 8.3 - Multi-Layer Networks

1. Backpropagation algorithm
Backpropagation (entry from Wikipedia)
Backpropagation neural network (tutorial from NeuroAI)
Demonstration of neural networks by Geoffrey Hinton (video)
Learn to design your own neural networks

2. Biological plausibility
Some myths of connectionism (paper by Istvan Berkeley, 1997)
Connectionism, confusion, and cognitive science (paper by Michael Dawson and Kevin Shamanski, 1994; originally from The Journal of Intelligent Systems, 4)

Sections 8.4 - Information Processing in Neural Networks: Key Features

1. Distinctive features of neural networks
Distributed representation (entry by Chris Eliasmith; from the Dictionary of Philosophy of Mind)
Connectionism (entry by Jonathan Waskan; from the Internet Encyclopedia of Philosophy)
Philosophy of connectionism (bibliography from MindPapers)

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