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Functional diversity of single neurons in anatomically complex cortical networks

Final Report Summary - NEUROHUBS (Functional diversity of single neurons in anatomically complex cortical networks)

Our main goal was to establish an understanding of the processes of transformation of signals from the physical world into the mental one as learning occurs and behavior changes. We focused our studies on the transformation of sounds into brain representations and how these change with experience. Our work focused on the cortex, which is a brain region intimately involved in sensation, perception and learning. Given the immense complexity of the cortex, we hypothesized that it functions as a hub by connecting, dynamically, different subnetworks in the brain. The grant allowed us to develop new technologies and implement them in order to access brain subnetworks in both high and low resolution simultaneously. The goal was to study how the auditory cortex represents complex sounds in two models – maternal behavior and perceptual learning.

To study maternal plasticity, we measured how the brain represents simple sounds like pure tones and complex sounds such as vocalization. We developed and calibrated a novel method in mice called TRAP (in collaboration with Liqun Luo from Stanford university) that allowed us to screen and target single neurons across the brain that respond to specific sounds. Using this method, we identified specific brain regions that overrepresent complex sounds that are salient to the animal. For example, we played pup cries to mothers and screened for specific brain regions that respond robustly to those sounds. We discovered a brain region that was not known before to be involved in processing salient vocalizations. This region, called Temporal Association Cortex (TeA), was then analyzed in more detail. We then combined anatomical tracing with TRAP and we revealed the specific subnetworks that connect between TeA and other cortical regions. Indeed, silencing these specific subnetworks caused mothers to mis-identify their own pups. To date, we identified numerous potential brain subnetworks that could be important for parental care.

To study perceptual learning, we developed a new training apparatus for mice called the Educage. The Educage is built for high efficiency training of groups of mice on difficult perceptual tasks that take long periods of time to achieve with minimal human intervention. We trained mice to their perceptual limits and then measured responses from neurons in the primary auditory cortex. We describe a well-known signature of change in brain representation after learning simple sounds. However, using novel analyses (in collaboration with Haim Sompolinsky from Hebrew University), we show that the measured changes do not necessarily improve the performance of the brain to encode these simple sounds. We then trained mice to learn to discriminate among complex sounds that are more natural to the animal and show that the measured changes in brain activity actually improves the performance of the brain to encode these complex learned signals.