Mapping of spatiotemporal code features to neural and perceptual spaces

项目总结

Project Summary Two of the most fundamental questions of sensory neuroscience are: 1) how is stimulus information represented by the activity of populations of neurons at different levels of information processing? and 2) what features of this activity are read at the next levels of neural processing to guide behavior? The first question has been the subject of a large body of work across different sensory stimuli. To answer the second question, one needs to establish a causal link between neuronal activity and behavior. In many systems sensory information is represented by complex spatiotemporal patterns of neuronal activity. Novel recording and stimulating technology will soon allow the precise temporal control of hundreds and thousands of individual neurons, however, conceptual approaches of finding relevance of different spatiotemporal features of neural code still lag behind. To develop a new approach we chose the mammalian olfaction as a model system, because odor stimuli evoke complex patterns of glomerular activity with spatial and temporal scales fully compatible with existing imaging and pattern stimulation technologies. In addition, the accessibility of the cells in the next processing level, the mitral/tufted cells which get input from olfactory glomeruli and transmit the signal to higher brain areas, allows a systematic study of encoding different features of neural activity with known behavioral relevance. We propose a novel approach to map spatiotemporal code features to neural and perceptual spaces. First, we substitute sensory-driven neural activation by artificial and fully parametrized optogenetic pattern stimulation. By varying the parameters of such stimulation and recording the behavioral outcomes of the stimulation, we will build a detailed empirically-validated mathematical model of the relevance of different features of neural activity. Then we will test this model for natural odor stimuli, and explore how these features are processed and encoded by the next level of processing. Successful execution of the project will produce the first (to our knowledge) causally validated model for behavioral relevance of a distributed neural code. It will shine light on long standing questions in olfactory processing, approaching the olfactory code from the perspective of its behavioral relevance. The proposed approach can be further applied to different neural systems using multi-neuronal recording and stimulation techniques.