Hildebrand, D. G. | Search bioRxiv

bioRxiv2025

Recurrent connectivity supports carbon dioxide sensitivity in Aedes aegypti mosquitoes

The mosquito Aedes aegyptis human host-seeking behavior depends on the integration of multiple sensory cues. One of these cues, carbon dioxide (CO2), gates odorant and heat pathways and activates host-seeking behavior. The neuronal circuits underlying processing of CO2 information remain unclear. We used automated serial-section transmission electron microscopy (EM) to image and reconstruct the circuitry of the glomeruli that are innervated by the Ae. aegypti maxillary palp, including the glomerulus that responds to CO2. Notably, CO2-sensitive olfactory sensory neurons (OSNs) make high levels of recurrent synaptic connections with one another, while making a low density of feedforward synapses. At some of these contacts between CO2 OSNs, we observe ribbon- like presynaptic structures, which may further enhance recurrent signaling. We compared both feedforward and recurrent connectivity with all olfactory glomeruli in Drosophila melanogaster, and we found more recurrent connections between the Ae. aegypti CO2-responsive OSNs than in any D. melanogaster glomeruli. We developed a computational circuit model that demonstrates recurrent synapses are necessary for robust CO2 detection under normal physiological conditions. Together, elevated levels of recurrent connectivity and ribbon-like structures may amplify sensory information detected by CO2-sensitive OSNs to support mosquito activation and sensitization by CO2, even in the presence of high levels of other odorants in the environment. We propose that this circuit organization supports the salience of CO2 as a mosquito host cue. One Sentence SummaryConnectomic analysis of carbon dioxide circuitry in the disease-vector mosquito Aedes aegypti.

neuroscience↗

bioRxiv2021

Structured connectivity in the cerebellum enables noise-resilient pattern separation

The cerebellum is thought to detect and correct errors between intended and executed commands1-3 and is critical for social behaviors, cognition and emotion4-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the networks first layer8-13. However, maximizing encoding capacity reduces resiliency to noise7. To understand how neuronal circuits address this fundamental tradeoff, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy (EM) and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest these redundant, non-random connectivity motifs increase discriminability of similar input patterns at a minimal cost to the networks overall encoding capacity. This work reveals how neuronal network structure can balance encoding capacity and redundancy, unveiling principles of biological network architecture with implications for artificial neural network design.

neuroscience↗