Special Neural Engineering Seminar (Wednesday, Oct. 18, 2017)

Event Time and Location: Wednesday, October 18th @ 3PM in Steinman Hall Rm 402

Dr. Qi Wang (Department of Biomedical Engineering, Columbia University), Top-down and bottom-up modulation of neural coding in the somatosensory thalamus.

Abstract: The transformation of sensory signals into spatiotemporal patterns of neural activity in the brain is critical in forming our perception of the external world. Physical signals, such as light, sound, and force, are transduced to neural electrical impulses, or spikes, at the periphery, and these spikes are subsequently transmitted to the neocortex through the thalamic stage of the sensory pathways, ultimately forming the cortical representation of the sensory world. The bottom-up (by external stimulus properties) or top-down (by internal brain state) modulation of coding properties of thalamic relay neurons provides a powerful means by which to control and shape information flow to cortex. My talk will focus on two topics. First, I will show that sensory adaptation strongly shapes thalamic synchrony and dictates the window of integration of the recipient cortical targets, and therefore switches the nature of what information about the outside world is being conveyed to cortex. Second, I will discuss how the locus coeruleus – norepinephrine (LC-NE) system modulates thalamic sensory processing. Our data demonstrated that LC activation increased the feature sensitivity, and thus information transmission while decreasing their firing rate for thalamic relay neurons. Moreover, this enhanced thalamic sensory processing resulted from modulation of the dynamics of the thalamorecticulo-thalamic circuit by LC activation. Taken together, an understanding of the top-down and bottom-up modulation of thalamic sensory processing will not only provide insight about neurological disorders involving aberrant thalamic sensory processing, but also enable the development of neural interface technologies for enhancing sensory perception and learning.

Neural Engineering