Publications by authors named "Logan A Thomas"

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity.

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The cerebellum is thought to help detect and correct errors between intended and executed commands and is critical for social behaviours, cognition and emotion. 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 noise. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer.

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To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs.

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Article Synopsis
  • X-ray holographic nano-tomography (XNH) offers a new technique for imaging large volumes of neuronal networks at sub-100-nm resolution, addressing limitations of traditional light and electron microscopy.
  • This method enables detailed reconstruction of neuronal structures in both Drosophila and mouse nervous tissue, revealing important insights about synaptic inhibition in cortical cells.
  • By integrating XNH with machine learning techniques for automatic neuron segmentation, researchers can facilitate the analysis of complex neural circuits, paving the way for advancements in neuroscience.
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