How do auditory cortical neurons represent ethologically relevant natural stimuli? Characterizing stimulus feature selectivity and invariance

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How sensory systems represent natural signals is a long-standing classical problem in neuroscience. Using stimuli that are relevant to the animal produced the clearest descriptions of how sensory neurons work in "specialized animals", such as bats, the electric fish, and the barn owl. In each of these examples, the stimuli were both natural and simple, which was key to understanding their representations. Most of the sensory cortex in other animals and humans, however, deals with real-life stimuli that are statistically complex. Our progress in understanding how cortical circuits represent complex stimuli, such as speech and music, has been limited because standard statistical methods do not work well with complex stimuli. But today the situation has changed. Cutting-edge methods of receptive-field analysis that work well with any kind of natural stimuli and can discover complete representations have been recently developed, and I have tested them successfully in the auditory system of songbirds. We can at last investigate encoding in cortical neurons with stimuli that matter to animals, which will be decisive for understanding how the brain represents complex natural sounds.In this project, I propose to investigate at the single-neuron resolution the principles that govern these representations by neural circuits in auditory cortex. We will use mice, because unlike songbirds, they have auditory cortex. Like songbirds, mice sing to each other melodic songs (at frequencies that are too high for humans to hear). These ultrasonic vocalizations (USVs) form a part of flexible social communication in mice, and neurons in the mouse auditory cortex respond to them.We will address the following questions. 1) Does an individual neuron respond to several (many) different features of natural stimuli, or only to a single one? In other words, what is a neuron's receptive field? Computational models indicate that neuronal ensembles composed of diverse, mosaic receptive fields, are superior for encoding complex stimuli compared to populations in which each neuron responds only to a single stimulus feature. 2) What are the rules that govern how features are combined within a receptive field to achieve robust representations resistant to noise?To answer these questions, we will record responses of excitatory and inhibitory neurons to behaviourally relevant USVs in different layers of the auditory cortex sensitive to vocalizations. For comparison, we will also record responses to unfamiliar, behaviourally irrelevant birdsongs. We will then use the new statistical methods to compute the neurons' receptive fields.Next, we will take advantage of the latest advances in the field of machine learning and train state-of-the-art unsupervised neural networks to discover statistically optimal representations of these complex sounds. We will then compare these representations to those found in vivo. I expect that the artificial and natural representations of USVs (but not birdsongs) will involve the same or similar features, indicating that the brain represents vocalizations in a statistically optimal way.Finally, having identified features that drive individual cortical neurons, we will characterize whether they are combined within a receptive field in a way that helps to achieve representations that are resistant to acoustical noise-a fundamental property of animal and human hearing.The proposed work will advance our understanding of how the brain encodes natural sounds. The availability of the new statistical methods means that we can solve this long-standing problem now. Because USV communication is impaired in murine models of brain disorders accompanied by perturbed central auditory processing, such as autism spectrum disorders, understanding neuronal and computational mechanisms of central auditory processing in the mouse will help us understand both normal hearing and auditory and communication deficits in humans.