To identify mechanisms of predictive processing across the distributed thalamocortical circuit - Project Summary Many of the sounds that animals hear are created by their own actions and being able to correctly differentiate these sounds is critical to a range of behaviors. An influential idea is that the brain uses sensory-motor predictions to anticipate sounds generated by movement, and identifying the circuit mechanisms that learn and implement these predictions is critical to our understanding of cortical function in health and disease. Since predictive computations involve the interaction of sensory and non-sensory signals, identifying underlying circuit mechanisms will require understanding how distributed but interconnected brain regions work together. While the thalamus is often perceived as a simple conduit of sensory information, the second-order thalamus is tightly linked with both the sensory and motor cortex, positioning it to play a key role in integrating sensory and non- sensory information. This proposal will test the hypothesis that the auditory second-order thalamus shapes predictive processing throughout the auditory cortex. First, I will use a transgenic mouse line that specifically labels second-order thalamic neurons to map the precise functional connections of the second-order auditory thalamus (Aim 1, K99). Next, I will develop an acoustic augmented reality home cage environment where mice can rapidly learn multiple predictive behaviors. I will perform wireless recordings while freely moving mice make multiple sound-generating movements to determine the sensory, movement, and prediction information encoded in the second-order auditory thalamus (Aim 2, K99). Finally, I will perform simultaneous multi-area recordings and targeted neural interventions in the thalamus and cortex of behaving mice to determine how predictive computations are carried out across the thalamocortical circuit (Aim 3, R00). With the guidance of my mentorship team, I have developed a training plan at New York University that will provide me the technological skills needed to complete these aims and make important discoveries about how distributed circuits integrate sensory and non-sensory information during predictive processing. The proposed training plan will also provide me with the conceptual framework and professional skills to achieve my long-term career goal: to investigate how distributed circuits work together mechanistically to enable context-dependent auditory processing in health and disease as an independent scientist.
