Contents

2.1 Introduction

2.2 Background

2.2.1 Clocks

2.2.2 Previous Work

2.2.2.1 Connectionist Models

2.2.2.2 Learning Models

2.3 Proposal

2.3.1 Principles

2.3.1.1 An Adapted General Learning Model

2.3.1.2 Minimal Changes from the General

2.3.1.3 Maintain General Learning Performance While Also Allowing Timing

2.3.1.4 Robust Parameters

2.3.2 Architecture

2.3.3 Learning Algorithm

2.3.4 Parameters

2.4 Evidence

2.4.1 Basic Timing Phenomena

2.4.1.1 Fixed-Interval Timing

2.4.1.2 Peak-Interval Timing

2.4.2 Learning Phenomena

2.4.2.1 Learning the Time Marker

2.4.2.2 Parsimonious Timing

2.4.2.3 Initial Fixed-Interval Behavior

2.4.3 Complex Timing Phenomena

2.4.3.1 Gap Timing

2.4.3.2 Filled-Gap Timing

2.4.3.3 Scalar Timing

2.4.3.4 Possible Techniques for Scalar Timing

2.4.3.5 Noise and Variation 2.5 Discussion

2.5.1 Overall Performance

2.5.2 Difficulties

2.5.3 Future Research

2.5.4 Theoretical Implications References

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