| Preface | p. v |
| Opening and Themes | p. 1 |
| Introduction and Aspirations | p. 3 |
| Optimal Statistical Procedures | p. 9 |
| The optimisation of actions | p. 9 |
| Effective estimation of state | p. 12 |
| The quadratic/Gaussian case: Estimation and certainty equivalence | p. 13 |
| The linear model, in Bayesian and classic versions | p. 15 |
| Linear Links and Nonlinear Knots: The Basic Neural Net | p. 17 |
| Neural calculations: The linear gate and the McCulloch-Pitts net | p. 17 |
| Sigmoid and threshold functions | p. 21 |
| Iteration | p. 22 |
| Neural systems and feedback in continuous time | p. 26 |
| Equilibrium excitation patterns | p. 28 |
| Some special-purpose nets | p. 28 |
| Bifurcations and Chaos | p. 33 |
| The Hopf bifurcation | p. 33 |
| Chaos | p. 35 |
| Associative and Storage Memories | p. 41 |
| What is a Memory? The Hamming and Hopfield Nets | p. 43 |
| Associative memories | p. 43 |
| The Hamming net | p. 45 |
| Autoassociation, feedback and storage | p. 47 |
| The Hopfield net | p. 50 |
| Alternative formulations of the Hopfield net | p. 52 |
| Compound and 'Spurious' Traces | p. 54 |
| Performance and trace structure | p. 54 |
| The recognition of simple traces | p. 55 |
| Inference for compound traces | p. 57 |
| Network realisation of the quantised regression | p. 59 |
| Reliability constraints for the quantised regression | p. 60 |
| Stability constraints for the quantised regression | p. 62 |
| The Hopfield net | p. 64 |
| Preserving Plasticity: A Bayesian Approach | p. 69 |
| A Bayesian view | p. 69 |
| A robust estimation method | p. 72 |
| Dynamic and neural versions of the algorithm | p. 74 |
| The Key Task: the Fixing of Fading Data. Conclusions I | p. 76 |
| Fading data, and the need for quantisation | p. 76 |
| The probability-maximising algorithm (PMA) | p. 78 |
| Properties of the vector activation function F(z) | p. 81 |
| Some special cases | p. 83 |
| The network realisation of the full PMA | p. 85 |
| Neural implementation of the PMA | p. 89 |
| The PMA and the exponential family | p. 92 |
| Conclusions I | p. 93 |
| Performance of the Probability-Maximising Algorithm | p. 96 |
| A general formulation | p. 96 |
| Considerations for reliable inference | p. 98 |
| Performance of the PMA for simple stimuli | p. 100 |
| Compound stimuli: The general pattern | p. 103 |
| Compound stimuli in the Gaussian case | p. 107 |
| Other Memories - Other Considerations | p. 109 |
| The supervised learning of a linear relation | p. 109 |
| Unsupervised learning: The criterion of economy | p. 111 |
| Principal components | p. 112 |
| The learning of an optimal reduction | p. 115 |
| Dual variables, back-propagation and Hebb's rule | p. 117 |
| Self-organising feature maps | p. 120 |
| Other proposals | p. 124 |
| Oscillatory Operation and the Biological Model | p. 127 |
| Neuron Models and Neural Masses | p. 131 |
| The biological neuron | p. 131 |
| Neural masses: Reduced dynamics | p. 134 |
| Neural masses: Full dynamics and the tank/sump model | p. 137 |
| Systems of neural units (masses) | p. 142 |
| The Freeman oscillator | p. 142 |
| Numerical solution for the Freeman oscillator | p. 146 |
| The evidence of EEG traces | p. 148 |
| Freeman Oscillators - Solo and in Concert | p. 152 |
| Systems of neural units | p. 152 |
| The case of simple positive feedback | p. 157 |
| Systems of coupled oscillators | p. 159 |
| The Freeman oscillator with positive feedback | p. 160 |
| Numerical results for the oscillator with feedback | p. 165 |
| A block of oscillators | p. 168 |
| A chain of oscillators | p. 169 |
| Feedback plus standardisation: An essential mechanism | p. 170 |
| Numerical results for the standardised oscillator | p. 173 |
| Some notes on the literature | p. 175 |
| Associative Memories Incorporating the Freeman Oscillator | p. 179 |
| Safe data: Adaptation of the Bayesian inferential net | p. 179 |
| The oscillatory version of the one-stage PMA | p. 181 |
| The oscillatory version of the two-stage PMA | p. 184 |
| Numerical results for a two-stage system | p. 188 |
| Olfactory Comparisons. Conclusions II | p. 190 |
| The anatomy of the olfactory system | p. 191 |
| Neural components | p. 195 |
| Block versions and analogues of the olfactory system | p. 197 |
| Interpretation | p. 200 |
| Conclusions II | p. 205 |
| Transmission Delays | p. 208 |
| The Freeman oscillator with feedback | p. 208 |
| Interacting Freeman oscillators | p. 210 |
| Extension of the Wigner Semi-Circle Law | p. 213 |
| Realisation of the PMA equation | p. 216 |
| References | p. 219 |
| Index | p. 225 |
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