Research in computational neuroscience

Intensity, identity, and timing in odor coding

Whether it’s a single French roast in your kitchen or a dozen espresso machines at a Starbucks, coffee smells like coffee. Animals can disassociate the intensity from the identity of an odor – even though any given odor incites neural responses that are highly concentration dependent. To effectively parse their rapidly fluctuating chemical environment, olfactory circuits leverage adaptative mechanisms, timing, and synaptic connectivity.
We use Drosophila to investigate how temporal dynamics in olfactory receptor neurons (ORNs) shapes odor coding. How do different receptors filter odor stimuli? Is there a logic in the diversity of response dynamics observed for different odors? How do dynamics at the sensory input affect odor information processing and behavior?  Recently, we showed that temporal dynamics are largely independent of odor intensity and that individual ORNs adapt their sensitivity with the same scaling law. We have shown with theory and simulation that both of these properties may play a central role in background and concentration invariant odor coding  
Forging ahead, we are currently interested in testing the implications of odor coding for behavioral tasks. How do animals adjust their navigational strategies to distinct odor environments? How do they combine information across sensory modalities, and filter out misleading and redundant information? 
Our approach combines electrophysiology, optogenetics, statistical analysis, computation, and theory. We are in search of excellent graduate students and postdocs with backgrounds across the spectrum in systems and computational neuroscience – in particular, those with experience in optogenetics, machine learning, behavioral analysis, or biophysical modeling.