The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting
* Demonstrates the ongoing value and new potential of backpropagation
* Creates a wealth of sound mathematical tools useful across disciplines
* Sets the stage for the emerging area of fast automatic differentiation
* Describes new designs for forecasting and control which exploit backpropagation
* Unifies concepts from Freud, Jung, biologists, and others into a new mathematical picture of the human mind and how it works
* Certifies the viability of Deutsch's model of nationalism as a predictive tool--as well as the utility of extensions of this central paradigm
"What a delight it was to see Paul Werbos rediscover Freud's version of 'back-propagation.' Freud was adamant (in The Project for a Scientific Psychology) that selective learning could only take place if the presynaptic neuron was as influenced as is the postsynaptic neuron during excitation. Such activation of both sides of the contact barrier (Freud's name for the synapse) was accomplished by reducing synaptic resistance by the absorption of 'energy' at the synaptic membranes. Not bad for 1895! But Werbos 1993 is even better." --Karl H. Pribram Professor Emeritus, Stanford University
Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences.
Dynamic Feedback, Statistical Estimation, and Systems Optimization: General Techniques.
The Multivariate ARMA(1,1) Model: Its Significance and Estimation.
Simulation Studies of Techniques of Time-Series Analysis.
General Applications of These Ideas: Practical Hazards and New Possibilities.
Nationalism and Social Communications: A Test Case for Mathematical Approaches.
APPLICATIONS AND EXTENSIONS.
Forms of Backpropagation for Sensitivity Analysis, Optimization, and Neural Networks.
Backpropagation Through Time: What It Does and How to Do It.
Neurocontrol: Where It Is Going and Why It Is Crucial.
Neural Networks and the Human Mind: New Mathematics Fits Humanistic Insight.