Neurons and Symbols, The Stuff that Mind is Made Of
Igor Aleksander, Helen Morton, 1993

Preface
The authors are interested in the way that connectionism (the study of artificial neural systems) has affected and is likely to affect arguments in cognitive science (the science of thought). The point of view taken is that the addition of neural systems will extend older models and that connectionism will redirect the course of cognitive science.

The book takes the somewhat unusual stance of trying to discuss existing cognitive science and neural systems from the perspective of a common model. The model designed for this purpose is the 'Neural State Machine Model' (NSMM) .

Chapter 1. Introduction: The Stuff that Mind is Made Of
An overview of the issues covered by the book.

1.1 String, Sealing Wax, Neurons and Symbols

1.2 Connectionist Euphoria: The Return of Neurons

1.3 Artificial Intelligence - The House of Symbols
A simplified model of the artificial neuron is first presented. The model will be expanded in a
later chapter , but we start out slowly.

1.4 Cognition: A Symbolic Science?

1.5 Automata: The Ghosts of All Machines

1.6 The Great Debate

1.7 The Illusion in the Divide

1.8 Language and Neurons

1.9 Seeing and Thinking

1.10 Artificial Consciousness: A Framework for Cognitive Science

Chapter 2. Artificial Neural Nets
The chapter has introductory material on artificial neural networks, but presents that material in the form of a simplified description so as to make it more usable in cognitive modelling. For this reason, the authors recommend that the chapter not be skipped by those already knowledgeable about neural networks.

2.1 How to Get Rid of the Frills

2.2 The Minimal Neuron

2.3 Multi-layer Networks

2.4 Feedback: The Mechanism of the Inner Image

2.5 A General Neural Unit

2.6 The Simple Retrieval of Internal States

2.7 Input Sequences - The Beginnings of Language

2.8 Learning to Act

2.9 The Cognitive Properties of the General Neural Unit

Chapter 3. Artificial Intelligence
This chapter contains interoductory material on the subject of AI, artificial intelligence. It may be skipped by experts in that field.

3.1 The Fairground Machinery

3.2 The Turing Legacy

3.3 Intelligence and the Playing of Games

3.4 Mechanical Problem Solving

3.5 Logic and the Meaning of Sentences

3.6 Scripts in One's Head

3.7 The Wisdom of Experts

3.8 The Artificial Act of Seeing

3.9 Criticisms

3.10 A Neural Perspective on AI

Chapter 4. Cognitive Science
This chapter contains interoductory material on the subject of cognitive science. It may be skipped by experts in that field.

4.1 How New Is the Mind's New Science?

4.2 The Philosophy of Cognition

4.3 Psychology as Science

4.4 Cognitive Science - Logical Metaphors

4.5 Representing Knowledge

4.6 The Mind Machine

4.7 Memories Are Made of This

4.8 Solving More Problems

4.9 A Postscript on Cognitive Science

Chapter 5. Neural Automata
This chapter is largely devoted to classical automata theory, but the topic is looked at with neural systems in mind in a way that the computer formalist may find interesting.

5.1 Introduction: Automata - Not Zombies but Logical Machines

5.2 Neural Logic

5.3 The Formal Automaton

5.4 Synchonicity and Probabilism

5.5 Automata and Formal Languages

5.6 Non-Determinism and Other Little Problems

5.7 Working with Automata: From Identification to the Nature of Dreams

5.8 Genetic Automata

5.9 Standing Back

Chapter 6. The Great Debate
This chapter looks afresh at the debate that has arisen between 'connectionists' and 'classicists' in preparation for the material presented in chapter 7.

6.1 Introduction: An Alien's Tale

6.2 The Distributed Memory Model

6.3 The Initial Skirmish: A Question of Levels

6.4 Smolensky's Subsymbolic World

6.5 Cognitive Edifices Need Symbolic Bricks

6.6 A Few Points from the Arbiters

6.7 And the World Said ...

6.8 And, Philosophically Speaking ...

6.9 Looking Back

Chapter 7. The Divide is Illusory
This chapter outlines the 'Neural State Machine Model', leading to what the authors feel is a novel framework for the study of cognitive science.

7.1 Introduction: No Weights, Just State Machines

7.2 The Neural Automaton Revisited

7.3 The Weightless/Weighted Distinction

7.4 Emergent Properties

7.4.1 Pattern Recognition

7.4.2 Re-entrant States

7.4.3 Attractors

7.4.4 Memory

7.4.5 Input Temporal Sensitivity

7.4.6 Output Sequences

7.4.7 Unsupervised Learning

7.4.8 Capacity

7.5 Cognitive Competence

7.5.1 Pattern Recognition

7.5.2 Problem Solving

7.6 A Cognitive Neural Automaton

7.6.1 Base Structure

7.6.2 Explorative Learning Rules

7.6.3 Learning Under Instruction and Pre-Programming

7.6.4 Some Implications

7.7 Where Does This Leave Fodor and Pylyshyn's Attack?

7.7.1 Association Leads to Structure I

7.7.2 Association Leads to Structure II

7.7.3 Learning is More Important than Appreciating Probabilities

7.7.4 Neural Systems Transcend the 'Implementation Level' I

7.7.5 Neural Systems Transcend the 'Implementation Level' II

7.8 The Neural Extension to Cognitive Science

7.8.1 Learning

7.8.2 Mental Imagery

7.8.3 Cognitive Architectures

7.9 A Plethora of Algorithms?

Chapter 8. Language and Neurons

8.1 Introduction: Flexing the Linguistic Muscles of the NSMM

8.2 Symbolic versus Mental Views of Language

8.3 Experiments in the Past Tense

8.4 Who or What Broke the Window?

8.5 I Thought I Saw a Pussy Cat ...

8.6 Composability

8.7 Hidden Representations

8.8 The Language of thought

8.9 Looking Back: Language and Connectionism and Icons

Chapter 9. Seeing and Thinking

9.1 Introduction: Why Vision?

9.2 The Physics of Seeing and the Chemistry of Experience

9.3 Binding Forces

9.4 A Rose is a Rose is ...

9.5 Learning From Deficits

9.6 Going to the Movies

9.7 I Remember You ...

9.7.1 Short- and Long-Term Memories

9.7.2 Memory Capacity

9.7.3 Plasticity and Interference

9.8 Mental Imagery: The Measurements

9.9 Mental Imagery: Another Great Debate

9.9.1 Cognitive Penetrability

9.9.2 Qualia

9.9.3 Economy

9.9.4 Emotions and Moods

9.9.5 Interpretation

9.10 Looking Back at Seeing Things

Chapter 10. Neural Cognitive Science

10.1 A Fresh Look at Cognitive Scinece?

10.2 The Neural State Machine Model (NSMM) Revisited

10.2.1 Universality

10.2.2 The Variables

10.2.3 The Input State

10.2.4 The Output State

10.2.5 The Inner State

10.2.6 The Dynamics of State Changes

10.2.7 The Neural-Universal Continuum

10.2.8 Iconic Inner States

10.2.9 Time and Sequence

10.3 The World

10.4 Learning

10.4.1 Learning

10.4.2 Motivation for Learning

10.4.3 Understanding

10.4.4 Exploration

10.4.5 Instruction

10.4.6 Teaching Using Language

10.4.7 Programming

10.4.8 Generalization

10.5 Attention and Thought Control

10.5.1 Thought Hopping and Noise

10.5.2 Visual Attention

10.5.3 Auditory Attention

10.6 Language - Use and Evolution

10.7 Logic

10.8 Dennett's Consciousness

10.8.1 The Cartesian Theatre

10.8.2 Multiple Drafts

10.8.3 The Evolution of Consciousness

10.8.4 The Virtual Machines of Consciousness

10.8.5 Consciousness and the NSMM

10.9 A Kind of Epilogue


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