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Biological learning and control : how the brain builds representations, predicts events, and makes decisions / Reza Shadmehr and Sandro Mussa-Ivaldi
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Computational neuroscience | Computational Neuroscience SerPublisher: Cambridge, Mass : MIT Press, c2012Edition: Online-AusgDescription: Online-Ressource (1 online resource (385 p.)) : illISBN:- 9781283448918
- 1283448912
- 9780262301282
- 612.82
- 612.8/2 23
- QP376
- QP376 -- .S4373 2012eb
Contents:
Summary: A novel theoretical framework that describes a possible rationale for the regularity in how we move, how we learn, and how our brain predicts events.Summary: Intro -- Contents -- Series Foreword -- Introduction -- Chapter 1. Space in the Mammalian Brain -- 1.1 Where Am I? -- 1.2 Space Representations in the Mongolian Gerbil -- 1.3 Some General Properties of Space Maps in Psychology and Mathematics -- 1.4 Place Cells -- 1.5 Grid Cells -- 1.6 Grid Cells to Place Cells: Functional Analysis -- Summary -- Chapter 2. Building a Space Map -- 2.1 Ordinary Space -- 2.2 A Simple Model -- 2.3 Points and Lines -- 2.4 Distance and Coordinates -- 2.5 Deriving the Environment from Noise-Free Sensor Data -- 2.6 Rigid Motions and Homogeneous Coordinates -- 2.7 Updating the Space Model -- 2.8 Combining Process and Observation Models -- 2.9 Back to the Gerbils -- Summary -- Chapter 3. The Space Inside -- 3.1 Geometry vs. Dynamics -- 3.2 Does the Brain Compute Dynamics Equations? -- 3.3 The Engineering Approach -- 3.4 Does the Brain Represent Force? -- 3.5 Adapting to Predictable Forces -- 3.6 Another Type of State-Based Dynamics: Motor Learning -- Summary -- Chapter 4. Sensorimotor Integration and State Estimation -- 4.1 Why Predict Sensory Consequences of Motor Commands? -- 4.2 Disorders in Predicting the Sensory Consequences of Motor Commands -- 4.3 Combining Predictions with Observations -- 4.4 State Estimation: The Problem of Hiking in the Woods -- 4.5 Optimal Integration of Sensory Information by the Brain -- 4.6 Uncertainty -- 4.7 State Estimation and the Kalman Filter -- 4.8 Combining Predictions with Delayed Measurements -- 4.9 Hiking in the Woods in an Estimation Framework -- 4.10 Signal-Dependent Noise -- Summary -- Chapter 5. Bayesian Estimation and Inference -- 5.1 Bayesian State Estimation -- 5.2 Causal Inference -- 5.3 The Influence of Priors -- 5.4 The Influence of Priors on Cognitive Guesses -- 5.5 Behaviors That Are Not Bayesian: The Rational and the Irrational -- 5.6 Multiple Prior Beliefs -- Summary.PPN: PPN: 809535289Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PAD | ZDB-30-PQE
Contents -- Series Foreword -- Introduction -- Chapter 1. Space in the Mammalian Brain -- 1.1 Where Am I? -- 1.2 Space Representations in the Mongolian Gerbil -- 1.3 Some General Properties of Space Maps in Psychology and Mathematics -- 1.4 Place Cells -- 1.5 Grid Cells -- 1.6 Grid Cells to Place Cells: Functional Analysis -- Summary -- Chapter 2. Building a Space Map -- 2.1 Ordinary Space -- 2.2 A Simple Model -- 2.3 Points and Lines -- 2.4 Distance and Coordinates -- 2.5 Deriving the Environment from Noise-Free Sensor Data -- 2.6 Rigid Motions and Homogeneous Coordinates
2.7 Updating the Space Model -- 2.8 Combining Process and Observation Models -- 2.9 Back to the Gerbils -- Summary -- Chapter 3. The Space Inside -- 3.1 Geometry vs. Dynamics -- 3.2 Does the Brain Compute Dynamics Equations? -- 3.3 The Engineering Approach -- 3.4 Does the Brain Represent Force? -- 3.5 Adapting to Predictable Forces -- 3.6 Another Type of State-Based Dynamics: Motor Learning -- Summary -- Chapter 4. Sensorimotor Integration and State Estimation -- 4.1 Why Predict Sensory Consequences of Motor Commands? -- 4.2 Disorders in Predicting the Sensory Consequences of Motor Commands
4.3 Combining Predictions with Observations -- 4.4 State Estimation: The Problem of Hiking in the Woods -- 4.5 Optimal Integration of Sensory Information by the Brain -- 4.6 Uncertainty -- 4.7 State Estimation and the Kalman Filter -- 4.8 Combining Predictions with Delayed Measurements -- 4.9 Hiking in the Woods in an Estimation Framework -- 4.10 Signal-Dependent Noise -- Summary -- Chapter 5. Bayesian Estimation and Inference -- 5.1 Bayesian State Estimation -- 5.2 Causal Inference -- 5.3 The Influence of Priors -- 5.4 The Influence of Priors on Cognitive Guesses
5.5 Behaviors That Are Not Bayesian: The Rational and the Irrational -- 5.6 Multiple Prior Beliefs -- Summary -- Chapter 6. Learning to Make Accurate Predictions -- 6.1 Examples from Animal Learning -- 6.2 The LMS Algorithm -- 6.3 Learning as State Estimation -- 6.4 Prediction Errors Drive Adaptation of Internal Models -- 6.5 A Generative Model of Sensorimotor Adaptation Experiments -- 6.6 Accounting for Sensory Illusions during Adaptation -- 6.7 The History of Prior Actions Affects Patterns of Learning -- 6.8 Source of the Error -- Summary -- Chapter 7. Learning Faster
7.1 Increased Sensitivity to Prediction Errors -- 7.2 Modulation of Forgetting Rates -- Summary -- Chapter 8. The Multiple Timescales of Memory -- 8.1 Savings and Spontaneous Recovery of Memory -- 8.2 Two-State Model of Learning -- 8.3 Timescales of Memory as a Consequence of Adapting to a Changing Body -- 8.4 Passive and Active Metastates of Memory -- 8.5 Protection of Motor Memories -- 8.6 Multiple Timescales of Memory in the Cerebellum -- Summary -- Chapter 9. Building Generative Models: Structural Learning and Identification of the Learner -- 9.1 Structure of Dynamics for Two Example Systems
9.2 Evidence for Learning a Structural Model
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