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During locomotion, most vertebrates-and invertebrates such as Drosophila melanogaster-are able to quickly adapt to terrain irregularities or avoid physical threats by integrating sensory information along with motor commands. Key to this adaptability are leg mechanosensory structures, which assist in motor coordination by transmitting external cues and proprioceptive information to motor centers in the central nervous system. Nevertheless, how different mechanosensory structures engage these locomotor centers remains poorly understood. Here, we tested the role of mechanosensory structures in movement initiation by optogenetically stimulating specific classes of leg sensory structures. We found that stimulation of leg mechanosensory bristles (MsBs) and the femoral chordotonal organ (ChO) is sufficient to initiate forward movement in immobile animals. While the stimulation of the ChO required brain centers to induce forward movement, unexpectedly, brief stimulation of leg MsBs triggered a fast response and sustained motor activity dependent only on the ventral nerve cord (VNC). Moreover, this leg-MsB-mediated movement lacked inter- and intra-leg coordination but preserved antagonistic muscle activity within joints. Finally, we show that leg-MsB activation mediates strong avoidance behavior away from the stimulus source, which is preserved even in the absence of a central brain. Overall, our data show that mechanosensory stimulation can elicit a fast motor response, independently of central brain commands, to evade potentially harmful stimuli. In addition, it sheds light on how specific sensory circuits modulate motor control, including initiation of movement, allowing a better understanding of how different levels of coordination are controlled by the VNC and central brain locomotor circuits., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios., (© 2022. The Author(s).)

Information is encoded in neural networks through changes in synaptic weights. Synaptic learning rules involve a combination of rapid Hebbian plasticity and slower homeostatic synaptic plasticity that regulates neuronal activity through global synaptic scaling. Hebbian and homeostatic plasticity have been extensively investigated, whereas much less is known about their interaction. Here we investigated structural and functional consequences of homeostatic plasticity at dendritic spines of mouse hippocampal neurons. We found that prolonged activity blockade induced spine growth, paralleling synaptic strength increases. Following activity blockade, glutamate uncaging-mediated stimulation at single spines led to size-dependent structural potentiation: smaller spines underwent robust growth, whereas larger spines remained unchanged. Moreover, spines near the stimulated spine exhibited volume changes following homeostatic plasticity, indicating that there was a breakdown of input specificity following homeostatic plasticity. Overall, these findings demonstrate that Hebbian and homeostatic plasticity interact to shape neural connectivity through non-uniform structural plasticity at inputs., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity., New Method: We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features., Results: Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines., Comparison With Existing Methods: Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem., Conclusions: Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Connections between neurons can undergo long-lasting changes in synaptic strength correlating with changes in structure. These events require the synthesis of new proteins, the availability of which can lead to cooperative and competitive interactions between synapses for the expression of plasticity. These processes can occur over limited spatial distances and temporal periods, defining dendritic regions over which activity may be integrated and could lead to the physical rewiring of synapses into functional groups. Such clustering of inputs may increase the computational power of neurons by allowing information to be combined in a greater than additive manner. The availability of new proteins may be a key modulatory step towards activity-dependent, long-term growth or elimination of spines necessary for remodelling of connections. Thus, the aberrant growth or shrinkage of dendritic spines could occur if protein levels are misregulated. Indeed, such perturbations can be seen in several mental retardation disorders, wherein either too much or too little protein translation exists, matching an observed increase or decrease in spine density, respectively. Cellular events which alter protein availability could relieve a constraint on synaptic competition and disturb synaptic clustering mechanisms. These changes may be detrimental to modifications in neural circuitry following activity.