Our project will be focused on sensory representations in the auditory system with two overall objectives. First, we will adopt an experimental strategy that calls for optimal probing of the auditory system. We will determine how complex sound stimuli at different spatial locations are represented at multiple stages of the auditory pathway in awake marmoset monkeys by improving an optimal design approach recently made feasible by the collaborative research by the same team. This method works online, during neurophysiological recording, by generating sound stimuli that maximize the information gained about a hierarchical neural network model. A model attained from the online experiment provides an accurate description of complex auditory responses to rich sound stimuli in three dimensional space, and at the same time we also obtain a plausible network explanation of how complex response properties in inferior colliculus and auditory cortex might arise from combining multiple sound localization cues at lower levels in the auditory pathway. Second, we will start with the hypothesis that neural populations are optimized for real world situations, and we will determine whether the rich variety and heterogeneity of neuronal response properties measured in experiments can be explained by the hypothesis that they actually form an efficient population optimized for natural sounds. The expected outcome is a principled computational explanation for the complexity of neuronal populations for sound localization, including all the diversity and variability associated with various functional cell types. Intellectual merit: Understanding the relationship between neural activity and the stimuli from the external world is one of the basic goals in systems neuroscience. Our collaborative research may help resolve a longstanding problem in auditory neuroscience concerning how neuronal responses in auditory cortex and inferior colliculus represent space over the full 360? range of azimuths and elevations. Our approach is very general and should apply readily to other sensory modalities as well. In particular, the optimal design method can obtain a global picture of the stimulus-response landscape as fast as possible, and this speedy feature is especially valuable for working with awake and behaving animals, and even humans. Our results may extend to many disciplines whenever one needs to efficiently probe a parameterized input-output system, or to optimize a population of sensors. For example, the ideas and the methods developed here could be used to guide practical neuromorphic engineering designs or to optimize populations of artificial sensors, which may find many practical applications. Our results will also provide a solid conceptual basis and a set of modeling tools to qualify abnormal or diseased states of the auditory system at both the cortical and subcortical levels for processing of complex sound signals. This progress could lead to the development of viable therapies for various neurological disorders, and it could improve the development of neural prostheses by better parsing complex auditory scenes with the help of sound localization cues. Broader impact: This project contributes to teaching at several levels. It will provide research training to two graduate students and the results of these studies are expected to constitute the bulk of their Ph.D. theses. In addition, a postdoctoral fellow will receive research training in collaborative research in computational neuroscience. Efforts will be made to include participation from students and researchers over a wide demographic, and the positions will be broadly advertised to ensure that qualified underrepresented candidates are aware of the opportunities. This research will also be integrated into our educational efforts by incorporating results into courses developed for both graduate and undergraduate students at Johns Hopkins University. The training, educational and outreach components will directly affect a large number of students and other groups outside of the university by exposing them to open problems and interdisciplinary research methods in neurophysiology and computational neuroscience. The PI and co-PIs are committed to advancement of women and underrepresented minorities in research and education. Furthermore, the proposed work will strengthen our infrastructure for further studies by pioneering new recording techniques that are based on closed-loop automated stimulus design. The research will be disseminated in many venues, including national and international meetings and peer reviewed journals. When applicable, our results will be disseminated in popular, non-technical literature as well. All software associated with this work will be made freely available on the internet, and appropriate subsets of the data collected for this project will be made available on the CRCNS-funded data sharing facility.
|Doruk, R Ozgur; Zhang, Kechen (2018) Fitting of dynamic recurrent neural network models to sensory stimulus-response data. J Biol Phys 44:449-469|
|DiMattina, Christopher; Zhang, Kechen (2013) Adaptive stimulus optimization for sensory systems neuroscience. Front Neural Circuits 7:101|