Neural systems are remarkable in their ability to perform a diversity of coherent tasks such as sensory processing, information transfer, and information storage. Understanding how neuronal circuits interact to form coherent networks is challenging due to their complexity. In this project, the olfactory neural system in insects is studied as a model for vertebrate and human sensory neural circuits because it encodes environmental cues into coherent perceptual objects and electrophysiological recordings can be obtained while the invertebrate animal is behaving. This project will develop mathematical tools to use multineural recordings from the olfactory processing unit in moths to classify robust patterns and to infer a predictive computational model. The computational model will help to reveal the design principles upon which these neuronal circuit networks are built. Simulations generated by the model for complex and dynamic olfactory stimuli will be used to guide electrophysiological and behavioral experiments to quantify how the olfactory network responds, classifies, and recognizes these stimuli.
In this project electrophysiological recordings will be used to infer a dynamical network of the antennal lobe, the primary olfactory processing center in insects. For that purpose, a large dataset of neural recordings will be obtained, and mathematical tools that extend state-of-the-art data reduction and structure inference methods will be developed. These tools will be designed to optimally represent the multidimensional time-series data and infer the network structure. In particular, the research will be focused on constructing a decision space from data, spanned by population vectors (neural codes) each representing a stimulus, in which scents can be classified and recognized. By associating with each neuron a dynamical model, the network wiring that produces similar dynamics in the odor space will be inferred using optimization. With the predictive model and stimuli classification methods, this project will resolve questions regarding network design, such as why do the encoding dynamics appear to be robust even for noisy stimuli or how does the network structure, particularly inhibitory connections and feedback, produce robust patterns of neural activity. Optimal values of parameters and those that cause alteration of dynamics will be identified as a result of the study. Additionally, both the output of the model and electrophysiological recordings will be used to predict responses to dynamic inputs, background odor, and mixtures of odors.
