Creating Brain Like Intelligence From Basic Principles to Complex Intelligent Systems 1st ediiton by Bernhard Sendhoff, Edgar Körner, Olaf Sporns, Helge Ritter, Kenji Doya ISBN 3642006159  978-3642006159

Creating Brain-Like Intelligence From Basic Principles to Complex Intelligent Systems 1st ediiton by Bernhard Sendhoff, Edgar Körner, Olaf Sporns, Helge Ritter, Kenji Doya  – Ebook PDF Instant Download/Delivery. 3642006159  978-3642006159
Full download Creating Brain-Like Intelligence From Basic Principles to Complex Intelligent Systems 1st edition after payment

Product details:

ISBN 10: 3642006159
ISBN 13: 978-3642006159
Author: Bernhard Sendhoff, Edgar Körner, Olaf Sporns, Helge Ritter, Kenji Doya 

This volume features contributions based on the presentations given at the International Symposium Creating Brain-Like Intelligence, held in Hohenstein, Germany, February 2007. It represents the current state of the art in several research fields.

Creating Brain-Like Intelligence From Basic Principles to Complex Intelligent Systems 1st Table of contents:

Part I: Foundations of Intelligence

1. Understanding Intelligence

   • Defining Intelligence

   • Biological vs. Artificial Intelligence

   • General vs. Narrow Intelligence

2. The Brain as a Model for Intelligence

   • Overview of the Brain’s Structure and Function

   • Neurons and Synapses: The Basic Building Blocks

   • Cognitive Processes in the Brain

3. Neuroscience of Learning and Memory

   • Hebbian Learning

   • Long-Term Potentiation (LTP) and Plasticity

   • Memory Systems: Short-Term vs. Long-Term

4. Perception and Sensory Processing

   • Sensory Systems and Data Processing

   • Vision, Hearing, and Touch in the Brain

   • Sensory Integration and Multimodal Perception

---

Part II: Bridging Biology and Computation

5. From Neurons to Artificial Neurons

   • Biological Neurons vs. Artificial Neurons

   • Neuron Models: From McCulloch-Pitts to Spiking Neurons

   • Activation Functions and Learning Algorithms

6. Artificial Neural Networks: The Basics

   • Architecture and Components of ANNs

   • Feedforward Neural Networks and Backpropagation

   • Activation Functions and Optimization

7. Learning Algorithms and Cognitive Models

   • Supervised, Unsupervised, and Reinforcement Learning

   • Bio-Inspired Learning Techniques

   • Cognitive Architectures: ACT-R and Soar

8. Evolutionary Computation and Brain Evolution

   • Evolutionary Algorithms and Their Role in AI

   • Evolutionary Strategies in Brain-Like Intelligence

   • The Role of Genetic Algorithms in AI Development

---

Part III: Advanced Topics in Brain-Like AI

9. Deep Learning and Neural Networks

   • Deep Neural Networks (DNNs) and Their Biological Inspirations

   • Convolutional Neural Networks (CNNs) and Vision

   • Recurrent Neural Networks (RNNs) and Temporal Processing

10. Neurosymbolic AI: Bridging Perception and Reasoning

    • Combining Neural Networks and Symbolic AI

    • Cognitive Models and Symbolic Representations

    • Neural-Symbolic Integration for Higher-Level Reasoning

11. Neuroplasticity in AI Systems

    • Mechanisms of Plasticity in the Brain

    • Implementing Neuroplasticity in AI Models

    • Lifelong Learning and Adaptation in Artificial Systems

12. Brain-Inspired Robotics and Embodied Intelligence

    • Robotics Inspired by the Brain’s Sensorimotor System

    • Control Theory and Cognitive Robotics

    • Embodied Cognition in AI

---

Part IV: Towards General Artificial Intelligence

13. Building General AI from Brain Principles

    • Overview of AGI and Its Challenges

    • Cognitive and Emotional Models for AGI

    • Learning, Reasoning, and Problem-Solving in AGI

14. Integrating Multiple Cognitive Functions

    • Multimodal Perception and Decision Making

    • Memory, Attention, and Executive Functioning

    • Planning and Execution in Brain-Inspired Systems

15. Ethical Considerations in Brain-Like AI

    • Consciousness and Sentience in AI

    • Ethical Implications of Brain-Inspired AI

    • Ensuring Safety and Fairness in AI Systems

---

Part V: Future Directions and Challenges

16. Current Trends in Brain-Like AI Research

    • State-of-the-Art Models and Techniques

    • Hybrid AI Systems and Emerging Paradigms

    • Challenges in Scaling to Human-Level Intelligence

17. The Road to True Artificial Consciousness

    • Defining and Measuring Consciousness

    • Theories of Consciousness in AI

    • Philosophical and Practical Implications

18. Future Applications of Brain-Like AI

    • Healthcare, Robotics, and Autonomous Systems

    • Education and Personalized Learning

    • AI in Creative and Artistic Endeavors

---

Conclusion

• Summing Up: From Basic Principles to Intelligent Systems

• The Vision for the Future of Brain-Inspired AI

• Final Thoughts and Next Steps

References

• Cited Works and Further Reading

Index

People also search for Creating Brain-Like Intelligence From Basic Principles to Complex Intelligent Systems 1st:

creating intelligence

brain-based learning principles

creating minds howard gardner

principles of brain-based learning

key principles of brain-based learning are that ____