2.1 Introduction

2.2 Background

2.2.1 Clocks

2.2.2 Previous Work Connectionist Models Learning Models

2.3 Proposal

2.3.1 Principles An Adapted General Learning Model Minimal Changes from the General Maintain General Learning Performance While Also Allowing Timing Robust Parameters

2.3.2 Architecture

2.3.3 Learning Algorithm

2.3.4 Parameters

2.4 Evidence

2.4.1 Basic Timing Phenomena Fixed-Interval Timing Peak-Interval Timing

2.4.2 Learning Phenomena Learning the Time Marker Parsimonious Timing Initial Fixed-Interval Behavior

2.4.3 Complex Timing Phenomena Gap Timing Filled-Gap Timing Scalar Timing Possible Techniques for Scalar Timing 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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