Decoding of Context-Dependent Olfactory Behavior in Drosophila

Abstract

Odor information is encoded in the activity of a population of glomeruli in the primary olfactory center. However, how this information is decoded in the brain remains elusive. Here, we address this question in Drosophila by combining neuronal imaging and tracking of innate behavioral responses. We find that the behavior is accurately predicted by a model summing normalized glomerular responses, in which each glomerulus contributes a specific, small amount to odor preference. This model is further supported by targeted manipulations of glomerular input, which biased the behavior. Additionally, we observe that relative odor preference changes and can even switch depending on the context, an effect correctly predicted by our normalization model. Our results indicate that olfactory information is decoded from the pooled activity of a glomerular repertoire and demonstrate the ability of the olfactory system to adapt to the statistics of its environment.

The use of computational modeling to link sensory processing with behavior in Drosophila

“…Neural encoding models can be derived from recordings in fixed, non-behaving animals -these models can then be used for predicting neural responses to the sensory stimuli from behavioral data sets. Thus, computational models can serve as stand-ins for recording neural activity during behavior and thus facilitate overcoming experimental hurdles when linking neural codes and natural behaviors (Parnas et al 2013;Schulze et al 2015;Clemens et al 2015a;Badel et al 2016). Here, we detail this approach using data from Drosophila (both adults and larvae), and we discuss selected studies that highlight both the challenges and advantages associated with computational modeling in this model system.…”

The use of computational modeling to link sensory processing with behavior in Drosophila

“…Several studies of Drosophila have employed decoding models to investigate the relationship between chemosensory codes and behavior (Parnas et al 2013;Gepner et al 2015;Hernandez-Nunez et al 2015;Badel et al 2016;Bell and Wilson 2016). For example, (Hernandez-Nunez et al 2015) asked how the activity of gustatory receptor neurons (GRN) affects chemotaxis behavior.…”

“…Instead, the responses to pairwise OSN activation often equaled the responses to the stronger OSN in the pair, suggesting a "max" pooling of OSN activity by downstream circuits. In a similar study, (Badel et al 2016) used odorant stimulation -not optogenetic activation of individual OSNs -to directly link naturalistic glomerular codes for odors to odortaxis in flying Drosophila. They showed that the glomerular code is relatively linear, since the neuronal responses to mixtures can be predicted from the responses to their constituents.…”

“…That is, inactivation abolished the behavior while activation was sufficient to induce flies to move. Similiarly, the decoding model by (Badel et al 2016) (see above) linked the olfactory population code in the fly's antennal lobe to odortaxis and predicted that individual glomeruli have only a small impact on navigational decisions. Consistent with this notion, they found that inactivating single glomeruli negligibly changed behavioral responses.…”

A Molecular Odorant Transduction Model and the Complexity of Combinatorial Encoding in the Drosophila Antenna

“…The transformation maps the odorant-receptor binding rate tensor modulated by the odorant concentration profile and the odorant-receptor dissociation rate tensor into OSN spike trains, respectively. The resulting concentrationdependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropils, such as odorant recognition (Badel et al, 2016) and olfactory associative learning (Lin et al, 2014;Hige et al, 2015).…”