ORIGINAL RESEARCH article
Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1655462
Neuron synchronization analyzed through spatial-temporal attention
Provisionally accepted
- 1Duke University Department of Electrical and Computer Engineering, Durham, United States
- 2University of Washington Department of Biology, Seattle, United States
- 3Duke University Department of Computer Science, Durham, United States
- 4School of Life Sciences, Arizona State University, Tempe, United States
- 5Schools of Physics and Biological Sciences,Georgia Institute of Technology, Atlanta, United States
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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 inter-spike interval. To address this limitation, we performed in vivo neural ensemble recording in the primary olfactory center—the antennal lobe (AL) of the hawk moth Manduca 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 AL (local interneurons (LNs) and projection neurons (PNs)). Furthermore, by experimentally manipulating the excitatory–inhibitory 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.
Keywords: neural synchronization, Bio-inspired neural networks, Generative Model, Attention-mechanism, antennal lobe
Received: 27 Jun 2025; Accepted: 18 Sep 2025.
Copyright: © 2025 Yang, KC, Chen, Lei, Sponberg, Tarokh and Riffell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jeffrey A Riffell, jriffell@uw.edu
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