Odor intensity and gain control in the honey bee olfactory system

MARACHLIAN EMILIANO; AYELEN NALLY; RAMON HUERTA; LOCATELLI FERNANDO

Montevideo

Conferencia; International Conference for Neuroethology; 2016

Odorintensity and gain control in the honey bee olfactory systemEmilianoMarachlian 2,3, Ayelen Nally 1, Ramon Huerta 4,Fernando Locatelli 1,31. Departamento de Fisiología, Biología Molecular yCelular, FCEyN , UBA, Argentina2. Departamento de Física, FCEyN , UBA, Argentina.3.IFIByNE- CONICET, Argentina4.Biocircuits Institute, UCSD, USA. Animals rely on chemicalcues to extract ecologically relevant information from the environment. Thisinformation is detected and primarily encoded by the olfactory sensory neurons.Each specific odor recruits a particular combination of receptors that providesthe input for its internal representation. However, in natural conditions,meaningful odors are normally present at different concentrations that producedifferent activity patterns in terms of both intensity of activation andcombination of receptors. A problem is how the animals recognize the same odor acrossdifferent concentrations in spite of the different input patterns. We work onthe hypothesis that local inhibition at processing of the olfactory informationprovides the gain control that contributes to stabilize odor identity independentlyof its intensity. We use honey bees as model animal for studying odorgeneralization across intensities and to understand the neural computationsthat underlie generalization. In behavioral experiments we describe odor generalizationacross intensities. Using calcium imaging we measured the neural representationsof high and low odor concentrations in the antennal lobe. Using different GABA blockerswe describe the contribution of the local inhibitory network in stabilizing odorpatterns across intensities. We found that GABA-A and GABA-B componentscontribute differentially in terms of the functional range of odor intensityand in the temporal profile of the activation elicited by odors. The resultsare formalized in a computational model of the antennal lobe that providesdetail of the inhibitory network.