A visual motion detection circuit suggested by Drosophila connectomics

By Shin-ya Takemura, A. Bharioke, Zhiyuan Lu, Aljoscha Nern, S. Vitaladevuni, P. Rivlin, W. Katz, D. J. Olbris, Stephen M. Plaza, Philip Winston and 14 more. Nature, 2013

693 citations recorded by the source corpus.

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/421c04ed63e6deaadc4f5ae35eb1b64fdf005e5d

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Citations in the corpus

Network neuroscience (2017, background): “Among recent studies in C. elegans 12 , as well as in the Drosophila visual system 13 , are refined mappings of connections among sensory and effector neurons, whose network patterns have offered important insights into the specific behavioral contributions of network elements.”
Quantitative neuroanatomy for connectomics in Drosophila (2015, methodology): “We refer to this as “iterative,” as compared to consensus methods that combine multiple independent reconstructions (Helmstaedter et al., 2011; Takemura et al., 2013; Kim et al., 2014).”
Saturated Reconstruction of a Volume of Neocortex. (2015, background): “This work has provided new insights into the visual system (Anderson et al., 2011; Helmstaedter et al., 2011; Kim et al., 2014; Briggman et al., 2011; Bock et al., 2011; see also Takemura et al., 2013; Mishchenko et al., 2010).”
On simplicity and complexity in the brave new world of large-scale neuroscience. (2015, background): “Experimental neuroscience is entering a golden age marked by the advent of remarkable new methods enabling us to record ever increasing numbers of neurons [1, 2, 3, 4, 5, 6], and measure brain connectivity at various levels of resolution [7, 8, 9, 10, 11, 12, 13, 14], sometimes measuring both connec”
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit (2017, background): “Thus far, it has only been possible to perform neural circuit mapping by using electrophysiological [1, 2], or electron-microscopic [3, 4] techniques.”
Cell-type Specific Labeling of Synapses in vivo through Synaptic Tagging with Recombination (STaR) (2014, methodology): “For instance, Chklovskii and colleagues developed a state-of-the-art semiautomated pipeline to reconstruct a connectome of 379 neurons in 280 Neuron 81, 280–293, January 22, 2014 ª2014 Elsevier Inc. the fly visual system (Takemura et al., 2013).”
A connectome of a learning and memory center in the adult Drosophila brain (2017, methodology): “The samples were then freeze-substituted in a Leica EM AFS2 low temperature embedding system in 1% osmium tetroxide, 0.2% uranyl acetate and 5% water in 99% acetone with 1% methanol, for 3 days (Takemura et al., 2013).”
Could a Neuroscientist Understand a Microprocessor? (2016, background): “However, there are other systems, in particular man made ones that we do understand.”
A connectome and analysis of the adult Drosophila central brain (2020, background): “For example, the CT1 neuron performs largely independent computations in each branch (Meier and Borst, 2019), whereas estimates show that within the medulla, the delays within each neuron are likely not significant for single column optic lobe neurons, and hence the neurons likely perform only a sin”
Structured Connectivity in Cerebellar Inhibitory Networks (2014, background): “Many brain areas reveal signs of structured connectivity, in particular, in relation to their functional representation (Briggman et al., 2011; Helmstaedter et al., 2013; Ko et al., 2011; Maisak et al., 2013; Takemura et al., 2013).”