Grant: Discovery Grants Program - Individual

Mathematical models of learning tell us that there are two different ways to learn from experience. One way is to learn to associate a sensory input with a desired output. For example, when we learn to read as children we learn to make particular sounds when we see certain characters. This type of learning is called ""supervised learning"". In contrast, another way to learn is to pick out patterns in our experiences. For example, people who have a lot of experience with wine learn to identify particular patterns and categories in smells and tastes. This type of learning is called ""unsupervised learning"". Researchers in artificial intelligence have shown that when you combine supervised and unsupervised learning computers can learn more rapidly and generalize better. Intuitively, this makes sense: once a person has a great deal of experience with wine and knows about the patterns in smells and tastes, he or she can assign labels to these patterns, fruity, earthy, etc., which will aid learning about new wines in the future.**Despite the theoretical importance of combined supervised and unsupervised learning, neuroscientists know very little about how it works in the brain. In fact, one of the only examples neuroscientists have of this combined learning is when young animals begin to process the spatial relationships between sights and sounds. Deep in an evolutionarily ancient part of our brains we combine visual signals and auditory signals to locate objects in the world around us. Our ability to do this is not hardwired genetically; rather, it is something that we learn when we are young. First, we learn about the spatial patterns that exist in our visual sensations, i.e. we use unsupervised learning to locate objects with our eyes. After that, we learn how to associate the sounds we hear to the spatial information that our eyes provide, i.e. we use supervised learning to locate objects with our ears. For example, experiments have shown that if you raise an animal with goggles on its face that shift everything they see to the left, then the auditory maps of space in these deep brain regions will also be shifted to the left to match the information coming from the eyes.**Although we know that this ancient part of our brains combines supervised and unsupervised learning in this way, there is a great deal that we don't understand about how it accomplishes this. In particular, it is unclear how the brain ensures that our eyes teach our ears how to locate objects, rather than the other way around. Theoretically speaking, there is no reason why our auditory systems couldn't learn without visual inputs and then teach our visual systems how to locate objects; after all, congenitally blind people can accurately use their ears for spatial localization. So, what mechanism does the brain use to ensure that, when everything is normal, our eyes teach our ears? My laboratory's research will attempt to answer this long-standing question. To understand the complex interactions occurring within the brain we will use a combination of electrical recordings in live neurons, genetic tools for controlling neurons with light, and computer models. We will determine how visual and auditory inputs to these deep brain regions are altered at different ages of development, and we will use what we learn to answer the question of how visual inputs teach auditory inputs. This research will provide a critical and timely contribution to the study of learning in neural systems, helping to close a gap between our mathematical and neurobiological theories. In the future, this could help us develop new therapies for improving learning and enable brain-computer interfaces for linking our brains with artificial learning systems.