The reason I originally joined Sliman's lab is because I was fascinated by the work on the representation of texture in both the peripheral nerves and primary somatosensory cortex. Since joining, I have attempted to further this research by recording from additional brain structures and understanding how they encode tactile features.
Texture coding in higher order somatosensory cortices
Further ascending the tactile neuraxis, the representation of complex textures in higher cortical areas was poorly understood - with most research using relatively simple parametric textures (though with good reason). We sought to understand how texture information was extracted in the transition between the primary (S1) and secondary (S2) somatosensory cortices. We found that texture signals in S2 were contaminated with cognitive signals but that they could be isolated to maintain a similar level of performance as S1. Furthermore, these cognitive signals could be exploited to decode both the task variable and the animal's decision.
Sensory computations in the cuneate nucleus of macaques
One of the foundational projects from the lab was characterizing the responses of the primary afferent fibers in response to tactile stimulation. That left us with knowledge about how signals were generated and the abundance of research in cortex gave some insight into how the brain processes the information. What transformations the information undergoes as it ascends the medial lemniscal pathway (cuneate & thalamus), however, was unclear. One of the long-term projects had been to characterize what transformations are performed by the cuneate nucleus (the first synapse after the afferents). Along with Aneesha Suresh, a graduate student in the lab, we recorded from the cuneate and compared the responses to those of the afferents and cortical neurons to understand what operations presently attributed to cortex were in fact done by the cuneate.
Effect of scanning speed on texture-elicited vibrations
In the first paper from my postdoc I inherited a project where we had recorded vibrations from the skin as various textures had been passed over them at several speeds. Given that we are able to approximate the neural response to a given set of vibrations, we wanted to examine how one might be able to infer the vibrations induced by textures at a range of speeds so that predictions could be made without collecting exorbitant amounts of data. After confirming a consistent relationship in the spatial domain (right) we collected a new dataset where we extensively presented a subset of textures at many speeds. We then found that we could model the transition in frequency space, predicting the vibrations at any speed from a single reference speed.