Learning in the inhibitory network of the honeybee antennal lobe

SHRUTI JOSHI; SETH HANEY; FERNANDO LOCATELLI ; BRIAN SMITH; MAXIM BAZHENOV

JOURNAL OF COMPUTATIONAL NEUROSCIENCE

SPRINGER

Lugar: Berlin; Año: 2021 vol. 49 p. 35 - 36

0929-5313

A honeybee in search of food locates nectar producing flowers using floral aromas composed of many volatile compounds. However, nectar-producing and non-producing floral odors contain many of the same compounds. Hence, the honeybee faces a challenging task in determining the map between chemical sensing and reward prediction. This is further complicated by the fact that nectar production may change from season to season and environment to environment. This requires the olfactory system to be able to learn and relearn the association of reward with variable blends of volatile compounds. In this new study, we examine the mechanisms underlying the creation and modification of neural representations of natural odor blends in the early olfactory system ? antennal lobe (AL) ? using a combination of computational modeling and Ca2 + imaging of the honeybee AL in vivo. Based on previous immunological labeling that showed octopamine receptors (modulating reward) co-localized with GABA receptors [1], we modeled plasticity in the inhibitory AL network. Following our previous modeling work [2], rewarded odors caused GABA facilitation based on presynaptic firing rates, and non-rewarded odors caused GABA facilitation based on post-synaptic firing. We found that this inhibitory plasticity was sufficient to create many of the changes seen in vivo. This includes the shifting of odor mixtures due to reward, the adaptation to many unrewarded odor presentations, and changes in the representations of complex blends. Importantly, our model learned to discriminate between complex odor blends by expanding coding space in the dimensions that were maximally discriminatory (Fig. 1A), which have been observed in vivo. Our model further predicted that the cells representing chemical compounds common to both rewarded and non-rewarded odors face increased inhibition from both associative and non-associative plasticity. This combined action diminished the superfluous components, while increasing the discriminatory components of the neural code (Fig. 1C). This prediction was then verified in vivo by examining Ca2 + imaging data (Fig. 1B, D). We found that glomeruli that were common to many odor blends were suppressed by training and those that were unique to a single odor blend were enhanced. Analysis of a black-box graphical convolutional neural network revealed a similar pattern of relationships between odor percepts to that learned in the biophysical model. Our model demonstrates a learning paradigm where the inhibitory network reshapes coding space to suit the current task and environment. These findings suggest an efficient computational strategy for perceptual learning in complex natural odors through modification of the inhibitory network.