Biological learning and control : how the brain builds representations, predicts events, and makes decisions / Reza Shadmehr and Sandro Mussa-Ivaldi.

Format:

Book

Author/Creator:

Shadmehr, Reza.

Contributor:

Mussa-Ivaldi, Sandro.

Series:

Computational neuroscience.

Computational neuroscience

Language:

English

Subjects (All):

Brain.

Neuropsychology.

Brain--Mathematical models.

Physical Description:

1 online resource (397 p.)

Edition:

1st ed.

Place of Publication:

Cambridge, Mass. : MIT Press, ©2012.

Language Note:

English

Summary:

In this work, the authors present a theoretical framework for understanding the regularity of the brain's perceptions, its reactions to sensory stimuli, and its control of movements.

Contents:

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

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

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

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

Chapter 7. Learning Faster

7.1 Increased Sensitivity to Prediction Errors

7.2 Modulation of Forgetting Rates

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

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

9.3 Nonuniqueness of the Structure

9.4 Subspace Method: Intuitive Ideas

9.5 Subspace Analysis

9.6 Examples

9.7 Estimating the Noise

9.8 Identifying the Structure of the Learner

9.9 Expectation Maximization (EM)

Chapter 10. Costs and Rewards of Motor Commands

10.1 Voluntary Eye Movements

10.2 Expected Reward Discounts the Cost of the Motor Commands

10.3 Movement Vigor and Encoding of Reward

10.4 Motor Costs

10.5 Motor Noise and Variability in Performance

10.6 Maximizing Performance While Minimizing Effort

10.7 Motor Costs during a Movement

Chapter 11. Cost of Time in Motor Control

11.1 Temporal Discounting of Reward

11.2 Hyperbolic vs. Exponential Discounting of Reward.

11.3 A Cost for Movements

11.4 Optimal Control of Eye Movements

11.5 Cost of Time and Temporal Discounting of Reward

11.6 State-Dependent Value of a Stimulus

11.7 Why Hyperbolic Discounting of Reward?

Chapter 12. Optimal Feedback Control

12.1 Examples of Feedback-Dependent Motor Control

12.2 A Brief History of Ideas in Biological Control

12.3 Bellman Optimality Principle

12.4 Control Policy

12.5 The Interplay between State Estimation and Control Policy

12.6 Example: Control of Eye and Head During Head-Free Gaze Changes

12.7 Limitations

12.8 The Brain Finds a Better Way to Clear a Barrier

Appendix

Notes

References

Index.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

OCLC-licensed vendor bibliographic record.

ISBN:

0-262-30050-8

1-283-44891-2

9786613448910

0-262-30128-8

OCLC:

775571543

Publisher Number:

9786613448910