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1. Corollary discharge prevents signal distortion and enhances sensing during locomotion

   https://doi.org/10.1101/2021.02.15.431323  

   Skandalis, *; Lunsford, Elias T.; Liao*, James C. (February 2021, bioRxiv)

   null (Ed.)

   Sensory feedback during movement entails sensing a mix of externally- and self-generated stimuli (respectively, exafference and reafference). In many peripheral sensory systems, a parallel copy of the motor command, a corollary discharge, is thought to eliminate sensory feedback during behaviors. However, reafference has important roles in motor control, because it provides real-time feedback on the animal’s motions through the environment. In this case, the corollary discharge must be calibrated to enable feedback while avoiding negative consequences like sensor fatigue. The undulatory motions of fishes’ bodies generate induced flows that are sensed by the lateral line sensory organ, and prior work has shown these reafferent signals contribute to the regulation of swimming kinematics. Corollary discharge to the lateral line reduces the gain for reafference, but cannot eliminate it altogether. We develop a data-driven model integrating swimming biomechanics, hair cell physiology, and corollary discharge to understand how sensory modulation is calibrated during locomotion in larval zebrafish. In the absence of corollary discharge, lateral line afferent units exhibit the highly heterogeneous habituation rates characteristic of hair cell systems, typified by decaying sensitivity and phase distortions with respect to an input stimulus. Activation of the corollary discharge prevents habituation, reduces response heterogeneity, and regulates response phases in a narrow interval around the time of the peak stimulus. This suggests a synergistic interaction between the corollary discharge and the polarization of lateral line sensors, which sharpens sensitivity along their preferred axes. Our integrative model reveals a vital role of corollary discharge for ensuring precise feedback, including proprioception, during undulatory locomotion. 

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2. Diversification of cnidarian mechanosensory neurons across life cycle phases: evidence from Hydrozoa

   Nakanishi, Nagayasu; Takahash, Mako; Kumano, Gaku (May 2025, Integrative and Comparative Biology)

   Over the course of more than half a billion years of independent evolution, cnidarians (e.g. sea anemones, corals and jellyfishes) have evolved diverse, multicellular, mechanosensory structures ranging from tentacles of hydroids to gravity-sensors of moon jellyfish. The ectodermal epithelium of mechanosensory structures houses the mechanosensory neuron – known as the concentric hair cell – characterized by an apical mechanosensory apparatus consisting of a single cilium surrounded by one or multiple rings of microvilli/stereovilli. While distinct concentric hair cell types are known to occur within life-cycle-stage-specific structures such as the sea anemone tentacles, it is unclear whether diverse concentric hair cell types exist across life cycle phases of any cnidarian. Here we report evidence from the hydrozoan Cladonema pacificum that concentric hair cells of sedentary polyps are distinct from those of free-swimming medusae. By carrying out touch assays, we demonstrate that polyps and medusae exhibit distinct mechanosensory behaviors. Moreover, we find that concentric hair cells in the ectodermal epithelium of touch-sensitive regions in polyps differ from those in medusae in the morphology of apical sensory apparatuses. Furthermore, polyp-type concentric hair cells are not retained in the ectoderm of medusa buds, and medusa-type concentric hair cells begin to form de novo during medusa formation. Taken together, these findings suggest that distinct mechanosensitive behaviors of polyps and medusae are mediated by morphologically different sets of mechanosensory neurons that develop via life-cycle-stage-specific mechanisms. We propose that cell type diversification of mechanosensory neurons occurred not only within a given life cycle phase but across life cycle phases in cnidarian evolution. 

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3. Hair, there and everywhere: A comparison of bat wing sensory hair distribution

   https://doi.org/10.1002/ar.25176  

   Rummel, Andrea_D; Sierra, Melissa_M; Quinn, Brooke_L; Swartz, Sharon_M (February 2023, The Anatomical Record)

   Abstract Bat wing membranes are composed of specialized skin that is covered with small sensory hairs which are likely mechanosensory and have been suggested to help bats sense airflow during flight. These sensory hairs have to date been studied in only a few of the more than 1,400 bat species around the world. Little is known about the diversity of the sensory hair network across the bat phylogeny. In this study, we use high‐resolution photomicrographs of preserved bat wings from 17 species in 12 families to characterize the distribution of sensory hairs along the wing and among species. We identify general patterns of sensory hair distribution across species, including the apparent relationships of sensory hairs to intramembranous wing muscles, the network of connective tissues in the wing membrane, and the bones of the forelimb. We also describe distinctive clustering of these sensory structures in some species. We also quantified sensory hair density in several regions of interest in the propatagium, plagiopatagium, and dactylopagatia, finding that sensory hair density was higher proximally than distally. This examination of the anatomical organization of the sensory hair network in a comparative context provides a framework for existing research on sensory hair function and highlights avenues for further research. 

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4. Anion efflux mediates transduction in the hair cells of the zebrafish lateral line

   https://doi.org/10.1073/pnas.2315515120  

   Lunsford, Elias T; Bobkov, Yuriy V; Ray, Brandon C; Liao, James C; Strother, James A (December 2023, Proceedings of the National Academy of Sciences)

   Hair cells are the principal sensory receptors of the vertebrate auditory system, where they transduce sounds through mechanically gated ion channels that permit cations to flow from the surrounding endolymph into the cells. The lateral line of zebrafish has served as a key model system for understanding hair cell physiology and development, often with the belief that these hair cells employ a similar transduction mechanism. In this study, we demonstrate that these hair cells are exposed to an unregulated external environment with cation concentrations that are too low to support transduction. Our results indicate that hair cell excitation is instead mediated by a substantially different mechanism involving the outward flow of anions. Further investigation of hair cell transduction in a diversity of sensory systems and species will likely yield deep insights into the physiology of these unique cells. 

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5. A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

   https://doi.org/10.1145/3406093  

   Otoum, Safa; Kantarci, Burak; Mouftah, Hussein (July 2021, ACM Transactions on Internet Technology)

   Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of . 

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