Research

Neurophysiology

My earlier research focused on how proprioceptive signals influence muscle activity through reflex pathways. I was particularly interested in how muscle spindle feedback influences motor output through spinal cord circuits. Using a noisy tendon vibration we could estimate how the motor output is influenced by muscle spindle feedback across a wide range of physiologically relevant frequencies.

One finding was that vibrating the Achilles tendon could evoke responses not only in muscles around the ankle, but also in muscles acting at the knee, hip, and trunk. In related work, we found that these responses become weaker and less responsive to higher-frequency stimulation with age.

I also investigated how different patterns of tendon vibration affect stretch reflexes, finding that both periodic and noisy vibration suppress muscle responses to sudden stretches.

Behavioural Research

During my PhD, I became interested in how the nervous system uses proprioceptive information to guide voluntary movement. Much of my research involved target-directed reaching experiments, where I used muscle tendon vibration to alter the sensory information available to participants.

A key finding from this work was that proprioceptive processing appears to be flexible. Rather than treating proprioception as a single, homogeneous signal, the nervous system seems to integrate information from multiple sources, with their relative contributions depending on the sensory information available. This suggests that our sense of limb position is constructed from several sources of information rather than relying on any one signal.

Computational Modelling

My interest in computational modelling grew out of my PhD research. The behavioural experiments suggested that the nervous system integrates proprioceptive information from multiple sources, but I wanted to better understand how that integration might work and whether a model could explain the patterns we observed.

To explore this, I’ve been developing a Bayesian model of proprioceptive integration. The model treats different sources of sensory information as noisy estimates of limb position and combines them according to their relative reliability. My goal is to see whether this framework can reproduce the reaching errors observed in my experiments and help explain how different proprioceptive signals contribute to our sense of limb position.

Publications

For the full list of journal articles, conference presentations, and other research work, see my CV. You can also find my papers on Google Scholar.