We develop biophysical models of neuro-sensory integration in, for instance, the model organism Caenorhabditis elegans. Building on recent experimental findings of the neuron conductances and their resolved connectome, we have posited the first full dynamic model of the neural voltage excitations that allows for a characterization of input stimuli to behavioral responses. Thus a clear connection between receptory cell inputs to downstream motor-responses is found, showing that robust, low-dimensional bifurcation structures dominate neural pathwaths of activity. The underlying bifurcation structures discovered, i.e. an induced Hopf bifurcation, are critical in explaining behavioral responses such as swimming and crawling.
Neural codes in high-dimensional sensory systems produce low-dimensional embeddings of firing rate activity for known input stimulus. These stereotyped patterns of activity are the fundamental codes transmitting information in large networks of neurons. How such high-dimensional networks produce such robust patterns of activity, and how such patterns can be learned for new stimulus, are key questions being addressed in, for instance, olfactory processing in insects. We study not only the encoding space for odor classification, but also how new odors can be learned in such networks.