@article {68517,
	title = {Structured cerebellar connectivity supports resilient pattern separation.},
	journal = {Nature},
	volume = {613},
	year = {2023},
	month = {2023 Jan 01},
	pages = {543-549},
	abstract = {<p>The cerebellum is thought to help\&nbsp;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{\textquoteright}s first layer. However, maximizing encoding capacity reduces the resilience to noise. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy 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 that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.</p>
},
	issn = {1476-4687},
	doi = {10.1038/s41586-022-05471-w},
	author = {Nguyen, Tri M and Thomas, Logan A and Jeff L Rhoades and Ricchi, Ilaria and Yuan, Xintong Cindy and Sheridan, Arlo and Hildebrand, David G C and Funke, Jan and Regehr, Wade G and Lee, Wei-Chung Allen}
}
@article {65725,
	title = {Dense neuronal reconstruction through X-ray holographic nano-tomography.},
	journal = {Nature Neuroscience},
	volume = {23},
	year = {2020},
	month = {2020 Dec -1},
	pages = {1637-43},
	abstract = {<p>Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.</p>
},
	issn = {1546-1726},
	doi = {10.1038/s41593-020-0704-9},
	author = {Kuan, Aaron T and Phelps, Jasper S and Thomas, Logan A and Nguyen, Tri M and Han, Julie and Chen, Chiao-Lin and Azevedo, Anthony W and Tuthill, John C and Funke, Jan and Cloetens, Peter and Pacureanu, Alexandra and Lee, Wei-Chung Allen}
}
