Synaptic transmission parallels neuromodulation in a central food-intake circuit

Schlegel, Philipp; Texada, Michael J.; Miroschnikow, Anton; Schoofs, Andreas; Hückesfeld, Sebastian; Peters, Marc; Schneider-Mizell, Casey M; Lacin, Haluk; Feng, Li; Fetter, Richard D.; Truman, James W.; Cardona, Albert; Pankratz, Michael J.

doi:10.7554/elife.16799

Cited by 127 publications

(155 citation statements)

References 117 publications

Supporting

Mentioning

153

Contrasting

Order By: Relevance

“…In this study, we validated the reconstructions as previously described 11,55 , a method successfully used in multiple studies 11,55-60 . Briefly, in Drosophila , as in other insects, the gross morphology of many neurons is stereotyped and individual neurons are uniquely identifiable on the basis of morphology 61-63 .…”

Section: Methodsmentioning

confidence: 91%

The complete connectome of a learning and memory centre in an insect brain

Eichler

Litwin-Kumar

et al. 2017

Nature

Self Cite

459

701

View full text Add to dashboard Cite

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the Drosophila larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.

show abstract

Section: Methodsmentioning

confidence: 91%

The complete connectome of a learning and memory centre in an insect brain

Eichler

Litwin-Kumar

et al. 2017

Nature

Self Cite

459

701

View full text Add to dashboard Cite

show abstract

“…Neural reconstruction of microtubule-containing backbone neurites (see Schneider-Mizell et al, 2016) or gross morphology alone is often sufficient to classify a neuron based on expert identification or searching of existing LM datasets with NBLAST software (Costa et al, 2016). Neurons traced to classification were at a minimum reconstructed, with or without synapses, to the point at which their gross morphologies (i.e.…”

Section: Electron Microscopy Reconstruction: Tracing To Classificationmentioning

confidence: 99%

“…Traced neurons were imported into Blender using Philipp Schlegel's CATMAID-to-Blender plugin (https://github.com/schlegelp/CATMAID-to-Blender) (Schlegel et al, 2016). Classes were then separated into layers; their synaptic inputs and outputs were removed; and they were given unique colors.…”

Section: Electron Microscopy Skeleton Renderingmentioning

confidence: 99%

The neuroanatomical ultrastructure and function of a biological ring attractor

Turner-Evans

Jensen

Ali

et al. 2019

Preprint

103

View full text Add to dashboard Cite

Neural representations of head direction have been discovered in many different species. A large body of theoretical work has proposed that the dynamics associated with these representations might be generated, maintained, and updated by recurrent network structures called ring attractors. Ring attractor models rely on specific assumptions about the structure of excitatory and inhibitory connectivity. These assumptions have been difficult to test directly. We therefore performed electron-microscopy-based circuit reconstruction and RNA profiling of identified cell types in the heading direction system of Drosophila melanogaster to directly examine excitatory and inhibitory synaptic connectivity, generating a dataset that should serve as a reference for future functional studies of the network. Consistent with several theoretical models, we identified network motifs that have been hypothesized to maintain the heading representation in darkness, update it when the animal turns, and tether it to visual cues. Genetically targeted two-photon calcium imaging and thermogenetic perturbation of the constituent neuron types during behavior provided additional support for these functional roles. However, we also discovered network motifs absent in current models, including a surprising degree of recurrence between arbors of different neurons with mixed pre-and post-synaptic specializations. Overall, our results confirm that the Drosophila heading direction network contains the core components of a ring attractor while also revealing unpredicted structural features that might enable the heading system to accurately track the animal's heading with a small number of neurons. Importantly, however, these and other models of attractor networks in the fly have assumed the circuit's connectivity based on relatively indirect evidence (Cope et al., 2017;Han et al., 2019;Kakaria and de Bivort, 2017;Kim et al., 2017;Su et al., 2017;Turner-Evans et al., 2017). For example, the location of pre-and post-synaptic arbors has been inferred from whether neural processes visible in light-microscopic images seem spine-or bouton-like in specific substructures (Hanesch et al., 1989;Lin et al., 2013;Wolff et al., 2015). The hypothesized connectivity of the circuit has then been derived from light-level overlap between the putatively pre-and post-synaptic arbors of neurons . In Drosophila, such conclusions have been bolstered by employing GFP-reconstitution-acrosssynaptic-partners (GRASP) (Xie et al., 2017) and trans-Tango (Omoto et al., 2018) techniques. Although both GRASP and trans-Tango are powerful techniques, their reliability and accuracy when used to estimate pairwise connectivity is limited (Lee et al., 2017; Talay et al., 2017). Similarly, measurements of functional connectivity by optogenetic stimulation of one neuron type and calcium imaging of another (Franconville et al., 2018) cannot conclusively establish the presence or absence of monosynaptic connectivity.In the current study, we established the synaptic-and cellular-resolution str...

show abstract

“…f) Incoming synapse of a neuron in protocerebral bridge. Rendering in a,b was done with with C -to-blender (Schlegel et al, 2016)).…”

Section: Evaluation Proceduresmentioning

confidence: 99%

Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset

Buhmann

Sheridan

Gerhard

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and require only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: between 92% and 96% of edges linking connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up-and downstream neurons.

show abstract

Synaptic transmission parallels neuromodulation in a central food-intake circuit

Cited by 127 publications

References 117 publications

The complete connectome of a learning and memory centre in an insect brain

The complete connectome of a learning and memory centre in an insect brain

The neuroanatomical ultrastructure and function of a biological ring attractor

Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset

Contact Info

Product

Resources

About