Institute for Adaptive and Neural ComputationHumans and animals can plan and execute movements much more adaptably and
reliably than current computers can calculate robotic limb trajectories. Over recent
decades, it has been suggested that our brains use motor primitives as blocks
to build up movements. In broad terms a primitive is a segment of pre-optimised
movement allowing a simplified movement planning solution. This thesis explores
a generative model of handwriting based upon the concept of motor primitives.
Unlike most primitive extraction studies, the primitives here are time extended
blocks that are superimposed with character specific offsets to create a pen trajectory.
This thesis shows how handwriting can be represented using a simple fixed
function superposition model, where the variation in the handwriting arises from
timing variation in the onset of the functions. Furthermore, it is shown how handwriting
style variations could be due to primitive function differences between individuals,
and how the timing code could provide a style invariant representation
of the handwriting. The spike timing representation of the pen movements provides
an extremely compact code, which could resemble internal spiking neural
representations in the brain. The model proposes an novel way to infer primitives
in data, and the proposed formalised probabilistic model allows informative priors
to be introduced providing a more accurate inference of primitive shape and
timing