Connecting the Brain, Body, and Environment

by Hwu, Krichmar

ISBN: 9780262370547 | Copyright 2022

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An introduction to neurorobotics that presents approaches and design principles for developing intelligent autonomous systems grounded in biology and neuroscience.

Neurorobotics is an interdisciplinary field that draws on artificial intelligence, cognitive sciences, computer science, engineering, psychology, neuroscience, and robotics. Because the brain is closely coupled to the body and situated in the environment, neurorobots—autonomous systems modeled after some aspect of the brain—offer a powerful tool for studying neural function and may also be a means for developing autonomous systems with intelligence that rivals that of biological organisms. This textbook introduces approaches and design principles for developing intelligent autonomous systems grounded in biology and neuroscience. It is written for anyone interested in learning about this topic and can be used in cognitive robotics courses for students in psychology, cognitive science, and computer science.

Neurorobotics covers the background and foundations of the field, with information on early neurorobots, relevant principles of neuroscience, learning rules and mechanisms, and reinforcement learning and prediction; neurorobot design principles grounded in neuroscience and principles of neuroscience research; and examples of neurorobots for navigation, developmental robotics, and social robots, presented with the cognitive science and neuroscience background that inspired them. A supplementary website offers videos, robot simulations, and links to software repositories with neurorobot examples.

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Contents (pg. v)
Preface (pg. vii)
I. Neurorobot Background and Foundations (pg. 1)
1. Neurorobotics: Origins and Background (pg. 3)
1.1 Neurorobotics (pg. 3)
1.2 Early Examples of Neurorobots (pg. 4)
1.3 Robotics (pg. 8)
1.4 Neurorobotic Approach (pg. 14)
Notes (pg. 15)
2. Neuroscience: Background for Creating Neurorobots (pg. 17)
2.1 Introduction: The Need for Neuroscience (pg. 17)
2.2 Neurons and Synapses (pg. 17)
2.3 Systems Neuroscience (pg. 25)
2.4 The Neurorobotics Approach to Systems Neuroscience (pg. 33)
2.5 Case Study: Visual Navigation in Insects and Mammals (pg. 35)
2.6 Summary and Conclusions (pg. 45)
2.A Appendix (pg. 45)
3. Learning and Memory (pg. 49)
3.1 Introduction (pg. 49)
3.2 Learning Types (pg. 49)
3.3 Neural Network Basics (pg. 51)
3.4 Weight Stabilization (pg. 56)
3.5 Classical Conditioning and the Rescorla-Wagner Learning Rule (pg. 57)
3.6 Learning and Memory in Spiking Neural Networks (pg. 60)
3.7 Summary and Conclusions (pg. 63)
3.A Appendix (pg. 63)
4. Reinforcement Learning and Prediction (pg. 67)
4.1 Introduction (pg. 67)
4.2 Braitenberg Vehicle 4 (pg. 67)
4.3 Markov Decision Processes (pg. 68)
4.4 Reinforcement Learning (pg. 70)
4.5 Prediction (pg. 75)
4.6 Case Study: Darwin VII—Perceptual Categorization and Conditioning in a Brain-Based Device (pg. 78)
4.7 Summary and Conclusions (pg. 84)
II. Neurorobot Design Principles (pg. 87)
5. Neurorobot Design Principles 1: Every Action Has a Reaction (pg. 89)
5.1 Introduction (pg. 89)
5.2 Embodiment (pg. 89)
5.3 Efficiency through Cheap Design (pg. 92)
5.4 Sensory-Motor Integration (pg. 95)
5.5 Degeneracy (pg. 96)
5.6 Multitasking and Event-Driven Processing (pg. 100)
5.7 Case Study: Action Selection in Neurorobotic Model of the Basal Ganglia (pg. 104)
5.8 Summary and Conclusions (pg. 108)
6. Neurorobot Design Principles 2: Adaptive Behavior, a Change for the Better (pg. 113)
6.1 Introduction (pg. 113)
6.2 Learning and Memory (pg. 113)
6.3 Value (pg. 119)
6.4 Prediction (pg. 124)
6.5 Case Study: Schemas and Memory Consolidation in Robots (pg. 128)
6.6 Summary and Conclusions (pg. 133)
7. Neurorobot Design Principles 3: Behavioral Trade-Offs Because Life Is Full of Compromises (pg. 135)
7.1 Introduction (pg. 135)
7.2 Reward versus Punishment (pg. 135)
7.3 Invigorated versus Withdrawn (pg. 137)
7.4 Expected Uncertainty versus Unexpected Uncertainty (pg. 139)
7.5 Exploration versus Exploitation (pg. 141)
7.6 Foraging versus Defending (pg. 141)
7.7 Stress versus Calm (pg. 143)
7.8 Social versus Solitary (pg. 144)
7.9 Case Study: Anxious and Curious Behavior in a Neurorobot (pg. 145)
7.10 Summary and Conclusions (pg. 152)
III. Neurorobot Applications (pg. 153)
8. Neurorobotic Navigation (pg. 155)
8.1 Introduction (pg. 155)
8.2 Mapping (pg. 155)
8.3 Planning (pg. 162)
8.4 Case Study: Spiking Wavefront Propagation (pg. 165)
8.5 Case Study: Neurobiologically Inspired Robot Navigation and Planning (pg. 172)
8.6 Case Study: RatSLAM—an Application Oriented Model of Rodent Navigation (pg. 176)
8.7 Summary and Conclusions (pg. 181)
9. Developmental and Social Robotics (pg. 183)
9.1 Introduction (pg. 183)
9.2 The Psychology and Neuroscience of Development and Social Cognition (pg. 183)
9.3 Affective Robotics (pg. 186)
9.4 Imitation Learning (pg. 188)
9.5 Language (pg. 188)
9.6 Social Robotics: Applications and Outreach (pg. 191)
9.7 Case Study: Emotional Interactions (pg. 194)
9.8 Case Study: Grounding Actions to Words (pg. 198)
9.9 Summary and Conclusions (pg. 200)
10. Neurorobotics: Past, Present, and Future (pg. 205)
10.1 Introduction (pg. 205)
10.2 Summary and Takeaways (pg. 206)
10.3 Neurorobotics Challenges and Contributions (pg. 208)
10.4 Neurorobotics in Five, Ten, and Twenty Years (pg. 211)
10.5 Summary and Conclusions (pg. 214)
Glossary (pg. 215)
References (pg. 221)
Index (pg. 231)

Tiffany J. Hwu

Tiffany J. Hwu is a research scientist working on projects in autonomous agents and human-machine communication.

Jeffrey L. Krichmar

Jeffrey L. Krichmar is a Professor in the Department of Cognitive Sciences and the Department of Computer Science at the University of California, Irvine, where he teaches courses in artificial intelligence, cognitive robotics, and computational neuroscience.

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