Preface
Statistical Theory of Learning Curves p. 3
Generalization in Two-Layer Neural Networks p. 18
Annealed Theories of Learning p. 32
Mutual Information and Bayes Methods for Learning a Distribution p. 42
General Bounds for Predictive Errors in Supervised Learning p. 51
Perceptron Learning: The Largest Version Space p. 59
Large Scale Simulations for Learning Curves p. 73
Geometry of Admissible Parameter Region in Neural Learning p. 85
Learning by a Population of Perceptrons p. 94
On-Line Learning of Dichotomies: Algorithms and Learning Curves p. 105
The Bit-Generator and Time-Series Prediction p. 131
Phase Dynamics of Two and Three Coupled Hodgkin-Huxley Neurons under DC Currents p. 141
Periodic Synchronization in Networks of Neuronal Oscillators p. 156
Synchronization in Neural Networks with Finite Storage Capacity p. 167
The Cavity Method: Applications to Learning and Retrieval in Neural Networks p. 175
Storage Capacity of a Fully Connected Committee Machine p. 191
Thermodynamic Properties of the Multi-Neuron Interaction Model without Truncating the Interaction p. 198
Symmetry between Neuronal and Synaptic Dynamics of Neural Net p. 210
Learning and Maximum Entropy in General Boltzmann Machines p. 221
On the (Free) Energy of Stochastic and Continuous Hopfield Neural Networks p. 233
Neural Thermodynamics for Biological Ensembles p. 245
Learning Algorithms for Classification: A Comparison on Handwritten Digit Recognition p. 261
On the Consequences of the Statistical Mechanics Theory of Learning Curves for the Model Selection Problem p. 277
Learning of a Two-Layer Neural Network with Flexible Hidden Layer Size p. 285
Distributed Population Representation in Proprioceptive Cortex p. 294
Designing Cost Functions for Additional Network Functionality p. 305
Self-Organization of Gaussian Mixture Model for PDF Estimation p. 312