| Autore |
Downing Keith L
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Cambridge, MA : , : The MIT Press, , 2023
|
| Descrizione fisica |
1 online resource (280 pages)
|
| Disciplina |
006.3/2
|
| Collana |
The MIT Press
|
| Soggetto topico |
Neural networks (Computer science)
|
| ISBN |
0-262-37468-4
0-262-37467-6
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- 1. Introduction -- 1.1. Data from Predictions -- 1.2. Movement and Prediction -- 1.3. Adaptation and Emergence -- 1.3.1. Gradients and Emergence in Neural Networks -- 1.4. Overflowing Expectations -- 2. Conceptual Foundations of Prediction -- 2.1. Compare and Err -- 2.2. Guesses and Goals -- 2.3. Gradients -- 2.3.1. Gradients Rising -- 2.4. Sequences -- 2.5. Abstracting by Averaging -- 2.6. Control and Prediction -- 2.7. Predictive Coding -- 2.8. Tracking Marr's Tiers -- 3. Biological Foundations of Prediction -- 3.1. Gradient-Following Bacteria -- 3.2. Neural Motifs for Gradient Calculation -- 3.3. Birth of a PID Controller -- 3.3.1. Adaptive Control in the Cerebellum -- 3.4. Detectors and Generators -- 3.4.1. The Hippocampus -- 3.4.2. Conceptual Embedding in the Hippocampus -- 3.5. Gradients of Predictions in the Basal Ganglia -- 3.6. Procedural versus Declarative Prediction -- 3.7. Rampant Expectations -- 4. Neural Energy Networks -- 4.1. Energetic Basis of Learning and Prediction -- 4.2. Energy Landscapes and Gradients -- 4.3. The Boltzmann Machine -- 4.4. The Restricted Boltzmann Machine (RBM) -- 4.5. Free Energy -- 4.5.1. Variational Free Energy -- 4.6. The Helmholtz Machine -- 4.7. The Free Energy Principle -- 4.8. Getting a Grip -- 5. Predictive Coding -- 5.1. Information Theory and Perception -- 5.2. Predictive Coding on High -- 5.2.1. Learning Proper Predictions -- 5.3. Predictive Coding for Machine Learning -- 5.3.1. The Backpropagation Algorithm -- 5.3.2. Backpropagation via Predictive Coding -- 5.4. In Theory -- 6. Emergence of Predictive Networks -- 6.1. Facilitated Variation -- 6.2. Origins of Sensorimotor Activity -- 6.2.1. Origins of Oscillations -- 6.2.2. Activity Regulation in the Brain.
6.2.3. Competition and Cooperation in Brain Development -- 6.2.4. Layers and Modules -- 6.2.5. Running through the Woods on an Icy Evening -- 6.2.6. Oscillations and Learning -- 6.3. A Brief Evolutionary History of the Predictive Brain -- 7. Evolving Artificial Predictive Networks -- 7.1. I'm a Doctor, Not a Connectionist -- 7.2. Evolving Artificial Neural Networks (EANNs) -- 7.2.1. Reconciling EANNs with Deep Learning -- 7.3. Evolving Predictive Coding Networks -- 7.3.1. Preserving Backpropagation in a Local Form -- 7.3.2. Phylogenetic, Ontogenetic, and Epigenetic (POE) -- 7.4. Continuous Time Recurrent Neural Networks (CTRNNs) -- 7.4.1. Evolving Minimally Cognitive Agents -- 7.4.2. Cognitive Robots Using Predictive Coding -- 7.4.3. Toward More Emergent CTRNNs -- 7.5. Predictive POE Networks -- 7.5.1. Simulating Neural Selectionism and Constructivism -- 7.5.2. Predictive Constructivism -- 7.5.3. The D'Arcy Model -- 7.5.4. Neurites to Neurons in D'Arcy -- 7.5.5. Peripherals in D'Arcy -- 7.5.6. Neuromodulators in D'Arcy -- 7.5.7. Predictively Unpredictable -- 7.6. Most Useful and Excellent Designs -- 8. Conclusion -- 8.1. Schrodinger's Frozen Duck -- 8.2. Expectations Great and Small -- 8.3. As Expected -- 8.4. Gradient Expectations -- 8.5. Expecting the Unexpected -- References -- Index.
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| Record Nr. | UNINA-9910741380503321 |
Info
Gradient expectations : structure, origins, and synthesis of predictive neural networks / / Keith L. Downing
