bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.12.06.570338

Hybrid Neural Networks of the Olfactory Learning Center in the Drosophila Brain

Cheng, L.-S.·Charng, C.-C.·Chen, R.-H.·Feng, K.-L.·Chiang, A.-S.·Lo, C.-C.·Lee, T.-K.
View original source ↗Export metadata ↓

Abstract

Biological signal encoding is shaped by the underlying neural circuitry. In Drosophila melanogaster, the mushroom body (MB) houses thousands of Kenyon cells (KCs) that process olfactory signals from hundreds of projection neurons (PNs). Previous studies debated the connectivity between PNs and KCs (random vs. structured). Our multiscale analysis of electron microscopic data revealed a hybrid network with diverse synaptic connection preferences and input divergence across different KC classes. Using MB connectome data, our simulation model, validated via functional imaging, accurately predicted distinct chemical sensitivities in the major KC classes. The model suggests that the hybrid network excels in detecting food odors while maintaining precise odor discrimination in different KC classes. These findings underscore the computational advantages of this hybrid network.

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cheng, L.-S., Charng, C.-C., Chen, R.-H., Feng, K.-L., Chiang, A.-S., Lo, C.-C., Lee, T.-K.. 2023-12-06. Hybrid Neural Networks of the Olfactory Learning Center in the Drosophila Brain. https://doi.org/10.1101/2023.12.06.570338

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Attention Across Scales: From Individual Variation to Social Hierarchies and Brain Networks in Semi-Free-Ranging Macaques

Attention is a fundamental brain function supporting perception, decision-making, and social behavior, and its dysfunction profoundly impairs daily life. It is both dynamic and stable, varying across observations and individuals, changing across the lifespan, and being shaped by social and environmental experience. Yet capturing this complexity remains a central challenge in neuroscience. Here, we integrated longitudinal behavioral assessments of semi-free-ranging macaques living in naturalistic social groups with resting-state fMRI. We quantified performance across days, ages, and social hierarchies and related it to intrinsic brain organization. Distinct attentional phenotypes emerged, including individuals with reduced attentional control. Performance followed an inverted-U lifespan trajectory, improving from childhood to adulthood before declining. Social status modulated attentional performance. Critically, nonlinear lifespan trajectories and associations with individual attentional differences were most clearly expressed in frontoparietal connectivity. Together, these findings reveal how sustained attention is organized across scales, providing a biological framework for its individual diversity, social modulation, and neural basis.

neuroscience↗
bioRxiv2026

Decoding natural scenes from patterned optogenetic responses in mouse visual cortex

A central challenge in developing visual cortical prostheses is to determine how visual stimuli should be transformed into effective patterns of cortical stimulation. Although advances in stimulation technologies, including optogenetics, provide increasingly precise control over cortical activity, it remains unclear whether artificially evoked activity can reproduce the information content of naturally evoked visual representations. Here we establish a quantitative framework for evaluating visual encoding strategies by decoding cortical responses evoked by natural vision and patterned optogenetic stimulation. We developed a novel dual-modal paradigm in awake mice to bridge the gap between endogenous photostimulation and artificial network driving. By co-expressing the high-performance calcium indicator GCaMP6s and the red-shifted, ultra-sensitive opsin rsChRmine-oScarlet in the primary visual cortex (V1), we successfully translated dynamic natural movie frames into patterned, spatiotemporal optogenetic stimulation. Quantitative comparisons of macro-scale dynamics demonstrated that this patterned optogenetic injection evokes cortical states highly comparable and representationally aligned with those driven by actual visual photostimulation. To systematically evaluate the fidelity of these responses, we developed STAR, a deep learning model featuring spatial and temporal attention mechanisms, and successfully reconstructed the frames of natural movies from V1 signals under both experimental modalities. Collectively, our results demonstrate that complex sensory information can be both naturally encoded and synthetically injected into V1 circuits with high decoding fidelity. This work provides an empirical and computational proof-of-concept for intelligent, closed-loop biomimetic encoders, establishing a robust framework for next-generation cortical visual neuroprostheses and bidirectional brain-machine interfaces.

neuroscience↗
bioRxiv2026

Why Is Spontaneous Blink Timing Informative? An Adaptive Scheduling Perspective

Spontaneous eye blinks have long been linked to cognitive processing, yet how task demands shape blink timing and its relationship to behavioral performance remains unclear. We examined spontaneous blink behavior in 576 adults performing two variants of the Continuous Performance Task (CPT). Blink occurrence and timing were most strongly modulated by the experimental condition in the more demanding CPT-AX task, whereas their association with response time was stronger in the CPT-X task, where more consistent blink timing predicted faster responses. This dissociation suggests that task structure changes not only blink behavior but also the behavioral relevance of blink timing. These findings are consistent with an adaptive scheduling account of spontaneous blinking and provide a conceptual framework for understanding when and why blink timing contains chronometric information about ongoing cognition.

neuroscience↗