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Abstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee’s AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors. 

Neuronal synchronization refers to the temporal coordination of activity across populations of neurons, a process that underlies coherent information processing, supports the encoding of diverse sensory stimuli, and facilitates adaptive behavior in dynamic environments. Previous studies of synchronization have predominantly emphasized rate coding and pairwise interactions between neurons, which have provided valuable insights into emergent network phenomena but remain insufficient for capturing the full complexity of temporal dynamics in spike trains, particularly the interspike interval. To address this limitation, we performedin vivoneural ensemble recording in the primary olfactory center—the antennal lobe (AL) of the hawk mothManduca sexta—by stimulating with floral odor blends and systematically varying the concentration of an individual odorant within one of the mixtures. We then applied machine learning methods integrating modern attention mechanisms and generative normalizing flows, enabling the extraction of semi-interpretable attention weights that characterize dynamic neuronal interactions. These learned weights not only recapitulated the established principles of neuronal synchronization but also facilitated the functional classification of two major cell types in the antennal lobe (AL) [local interneurons (LNs) and projection neurons (PNs)]. Furthermore, by experimentally manipulating the excitation/inhibition balance within the circuit, our approach revealed the relationships between synchronization strength and odorant composition, providing new insight into the principles by which olfactory networks encode and integrate complex sensory inputs. 

Migratory locusts (Locusta migratoria) emit two key odorants during aggregation: 4-vinylanisole (4VA), which serves as an aggregation pheromone attracting conspecifics to form swarms, and phenylacetonitrile (PAN), which acts as an aposematic signal and a precursor of a defense toxin, deterring conspecifics from cannibalism and protecting against predators. However, how locusts reconcile these two conflicting olfactory signals while aggregating is not yet understood. Our study addresses this by examining the release dynamics of the two signals, their behavioral effects, and the neural mechanisms underlying their perception. 4VA is released earlier and at lower locust densities than PAN, with PAN’s release increasing as aggregation progresses. Although PAN’s emission levels eventually exceed those of 4VA, locusts consistently exhibit a preference for the emitted blend, regardless of variations in proportions and concentrations. Notably, increasing amounts of 4VA added to PAN can counteract PAN’s repellent effects, but this is not the case when PAN is added to 4VA. Mechanistically, we found that antennal neurons responsive to 4VA suppress the activity of neurons responsive to PAN. In the antennal lobe, it is the conduction velocities of projection neurons, rather than other neural properties, that are responsible for the observed behavioral pattern, leading to an overall attractive response. Collectively, our findings imply that insects are capable of harmonizing the effects of two distinct pheromones to optimize both social cohesion and chemical defense. 

Animals must learn to ignore stimuli that are irrelevant to survival and attend to ones that enhance survival. When a stimulus regularly fails to be associated with an important consequence, subsequent excitatory learning about that stimulus can be delayed, which is a form of nonassociative conditioning called ‘latent inhibition’. Honey bees show latent inhibition toward an odor they have experienced without association with food reinforcement. Moreover, individual honey bees from the same colony differ in the degree to which they show latent inhibition, and these individual differences have a genetic basis. To investigate the mechanisms that underly individual differences in latent inhibition, we selected two honey bee lines for high and low latent inhibition, respectively. We crossed those lines and mapped a Quantitative Trait Locus for latent inhibition to a region of the genome that contains the tyramine receptor geneAmtyr1[We use Amtyr1 to denote the gene and AmTYR1 the receptor throughout the text.]. We then show that disruption ofAmtyr1signaling either pharmacologically or through RNAi qualitatively changes the expression of latent inhibition but has little or slight effects on appetitive conditioning, and these results suggest that AmTYR1 modulates inhibitory processing in the CNS. Electrophysiological recordings from the brain during pharmacological blockade are consistent with a model that AmTYR1 indirectly regulates at inhibitory synapses in the CNS. Our results therefore identify a distinctAmtyr1-based modulatory pathway for this type of nonassociative learning, and we propose a model for howAmtyr1acts as a gain control to modulate hebbian plasticity at defined synapses in the CNS. We have shown elsewhere how this modulation also underlies potentially adaptive intracolonial learning differences among individuals that benefit colony survival. Finally, our neural model suggests a mechanism for the broad pleiotropy this gene has on several different behaviors.