Moving in time: a neural network model of rhythm-based motor sequence performance
Zeid, Omar Mohamed
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Many complex actions are precomposed, by sequencing simpler motor actions. For such a complex action to be executed accurately, those simpler actions must be planned in the desired order, held in working memory, and then enacted one-by-one until the sequence is complete. Examples of this phenomenon include writing, typing, and speaking. Under most circumstances, the ability to learn and reproduce novel motor sequences is hindered when additional information is presented. However, in cases where the motor sequence is musical in nature (e.g. a choreographed dance or a piano melody), one must learn two sequences at the same time, one of motor actions and one of the time intervals between actions. Despite this added complexity, humans learn and perform rhythm-based motor sequences regularly. It has been shown that people can learn motoric and rhythmic sequences separately and then combine them with little trouble (Ullén & Bengtsson 2003). Also, functional MRI data suggest that there are distinct sets of neural regions responsible for the two different sequence types (Bengtsson et al. 2004). Although research on musical rhythm is extensive, few computational models exist to extend and inform our understanding of its neural bases. To that end, this dissertation introduces the TAMSIN (Timing And Motor System Integration Network) model, a systems-level neural network model designed to replicate rhythm-based motor sequence performance. TAMSIN utilizes separate Competitive Queuing (CQ) modules for motoric and temporal sequences, as well as modules designed to coordinate these sequence types into a cogent output performance consistent with a perceived beat and tempo. Chapters 1-4 explore prior literature on CQ architectures, rhythmic perception/production, and computational modeling, thereby illustrating the need for a model to tie those research areas together. Chapter 5 details the structure of the TAMSIN model and its mathematical specification. Chapter 6 presents and discusses the results of the model simulated under various circumstances. Chapter 7 compares the simulation results to behavioral and imaging results from the experimental literature. The final chapter discusses future modifications that could be made to TAMSIN to simulate aspects of rhythm learning, rhythm perception, and disordered productions, such as those seen in Parkinson’s disease.
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