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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.
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Inferring Network Structure from Neural Activity in a Biologically-Constrained Model of the Insect Olfactory System
Understanding olfactory processing in insects requires characterizing the complex dynamics and connectivity of the first olfactory relay - antennal lobe (AL). We leverage in vivo electrophysiology to train recurrent neural network (RNN) model of the locust AL, inferring the underlying connectivity and temporal dynamics. The RNN comprises 830 projection neurons (PNs) and 300 local neurons (LNs), replicating the locust AL anatomy. The trained network reveals sparse connectivity, with different connection densities between LNs and PNs and no PN-PN connections, consistent with in vivo data. The learned time constants predict slower LN dynamics and diverse PN response patterns, with low and high time constants correlating with early and late odor-evoked activity, as reported in vivo. Our approach demonstrates the utility of biologically-constrained RNNs in inferring circuit properties from empirical data, providing insights into mechanisms of odor coding in the AL.
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- Award ID(s):
- 2323241
- PAR ID:
- 10627550
- Publisher / Repository:
- https://2024.ccneuro.org/
- Date Published:
- Format(s):
- Medium: X
- Location:
- Boston, MA
- Sponsoring Org:
- National Science Foundation
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Morozov, Alexandre V. (Ed.)Recent advances in molecular transduction of odorants in the Olfactory Sensory Neurons (OSNs) of theDrosophilaAntenna have shown that theodorant object identityis multiplicatively coupled with theodorant concentration waveform. The resulting combinatorial neural code is a confounding representation of odorant semantic information (identity) and syntactic information (concentration). To distill the functional logic of odor information processing in the Antennal Lobe (AL) a number of challenges need to be addressed including 1) how is the odorantsemantic informationdecoupled from thesyntactic informationat the level of the AL, 2) how are these two information streams processed by the diverse AL Local Neurons (LNs) and 3) what is the end-to-end functional logic of the AL? By analyzing single-channel physiology recordings at the output of the AL, we found that the Projection Neuron responses can be decomposed into aconcentration-invariantcomponent, and two transient components boosting the positive/negative concentration contrast that indicate onset/offset timing information of the odorant object. We hypothesized that the concentration-invariant component, in the multi-channel context, is the recovered odorant identity vector presented between onset/offset timing events. We developed a model of LN pathways in the Antennal Lobe termed the differential Divisive Normalization Processors (DNPs), which robustly extract thesemantics(the identity of the odorant object) and the ON/OFF semantic timing events indicating the presence/absence of an odorant object. For real-time processing with spiking PN models, we showed that the phase-space of the biological spike generator of the PN offers an intuit perspective for the representation of recovered odorant semantics and examined the dynamics induced by the odorant semantic timing events. Finally, we provided theoretical and computational evidence for the functional logic of the AL as a robustON-OFF odorant object identity recovery processoracross odorant identities, concentration amplitudes and waveform profiles.more » « less
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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.more » « less
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