Computational Templates, Neural Network Dynamics, and Symbolic Logic

This paper looks at the relationship between subsymbolic neural networks and symbolic logical systems from a philosophy of science perspective. More specifically, the point of view is that of Paul Humphreys´ philosophical account of the organization of scientific knowledge [Humphreys, Paul. Extending Ourselves -Computational Science, Empiricism and the Scientific Method. Oxford: Oxford University Press, 2004.] Humphreys considers the units of analysis constituting scientific knowledge (in computational science) to be computational models, and computational templates. Computational templates are abstract syntactic schemata underlying domain specific models. Humphreys´ own examples are mainly from computational research in physics, biology and statistics. Neural networks research in cognitive science can also be fruitfully considered from this computational models/templates perspective, illustrated here by two examples from recent neural networks research.