Rebecca Jordan is a systems neuroscientist endeavoring to understand how the brain integrates sensory and motor information to give rise to perception. Dr. Jordan has a B.A. (Honors) in natural sciences from the University of Cambridge. In 2013, she undertook a Ph.D. in the lab of Dr. Andreas Schaefer at the Francis Crick Institute in the United Kingdom, where she studied how behavior shapes early sensory processing in the olfactory bulb. From 2018 to 2022, Dr. Jordan was a postdoctoral fellow in Dr. Georg Keller’s lab at the Friedrich Miescher Institute in Switzerland. Here, using virtual reality systems, she investigated sensorimotor integration in the mouse primary visual cortex. Dr. Jordan’s current lab at the University of Edinburgh focuses on sensorimotor learning with an emphasis on the role of globally-projecting neuromodulator systems like the locus coeruleus, as well as the dysfunction of these learning processes in neurodevelopmental disorders.
“Cortical-catecholamine loops in sensorimotor learning”
When we move our eyes, the image of the world moves across the retina. Why, then, do we perceive a stable world? This question has intrigued neuroscientists for over a hundred years, yet the brain mechanisms are still unresolved. A potential solution to this kind of problem would be for the brain to integrate sensory and motor information in a manner consistent with predictive processing. Here, motor information about the upcoming movement is used to predict the sensory input that will result from it. This prediction is then subtracted from the incoming sensory input to leave only the discrepancies, termed prediction errors. Prediction errors have been found across the brain: famously in the dopaminergic ventral tegmental area (VTA) in the form of reward prediction errors, and more recently in the sensory cortex in the form of sensorimotor prediction errors. These signals are thought to be computationally useful for driving learning, which could happen at two levels: 1) in the cortex, prediction error neuron spiking could drive local synaptic plasticity, and 2) prediction errors broadcast by neuromodulators like the catecholamines (from the locus coeruleus and VTA) could modulate learning rates across the brain. We will investigate these ideas in the mouse, using multimodal virtual reality systems that enable precise coupling between animal motion and several kinds of sensory feedback. This allows us to manipulate the presumptive sensory expectations of the mouse during its movements to prompt learning, as well as violate these expectations to probe for prediction error responses. We will use a combination of in vivo electrophysiology, imaging, optogenetics and behavioral paradigms to assess the computational role of catecholamine systems in error-driven plasticity in the cortex and behavioral flexibility. This work is expected to greatly advance our mechanistic understanding of sensorimotor learning in the cortex and the function of catecholamine systems. This endeavor is important in the long term for understanding psychiatric conditions such as schizophrenia, in which both sensorimotor processing and catecholamine function appear to be disrupted.