A unified platform to manage, share, and archive morphological and functional data in insect neuroscience
- Lund University, Sweden
- University of Würzburg, Germany
- Norwegian University of Science and Technology, Norway
- University of Marburg, Germany
- Free University, Germany
- UiT The Arctic University of Norway, Norway
- Max Planck Institute for Chemical Ecology, Germany
Abstract
Insect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable. To promote an open data culture, we have therefore developed the InsectBrainDatabase (IBdb), a free online platform for insect neuroanatomical and functional data. The IBdb facilitates biological insight by enabling effective cross-species comparisons, by linking neural structure with function, and by serving as general information hub for insect neuroscience. The IBdb allow users to not only effectively locate and visualize data, but to make them widely available for easy, automated reuse via an application programming interface. A unique private mode of the database expands the IBdb functionality beyond public data deposition, additionally providing the means for managing, visualizing and sharing of unpublished data. This dual function creates an incentive for data contribution early in data management workflows and eliminates the additional effort normally associated with publicly depositing research data.
Data availability
All data underlying the figures of the paper are freely available in the insect brain database: insectbraindb.orgAccess to the database is free and can be achieved either by browsing insectbraindb.org or by API access. Documentation see https://insectbraindb.org/static/IBdb_Userguide.pdf
Article and author information
Author details
Frederick Zittrell
Funding
H2020 European Research Council (714599)
- Stanley Heinze
Swedish Research Foundation (2014 - 04623)
- Marie Dacke
H2020 European Research Council (817535)
- Marie Dacke
Air Force Office of Scientific Research (FA9550-14-1-0242)
- Eric Warrant
Deutsche Forschungsgemeinschaft (EL784/1-1)
- Basil el Jundi
Deutsche Forschungsgemeinschaft (HO 950/24-1,HO 950/25-1,HO 950/26-1)
- Uwe Homberg
Deutsche Forschungsgemeinschaft (Me365/34)
- Randolf Menzel
Startup grant from the University of Würzburg
- Keram Pfeiffer
Norwegian Research Council (287052)
- Bente G Berg
Freie Universität Berlin and Zukunftskolleg University Konstanz
- Randolf Menzel
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Heinze et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,984
- views
-
- 293
- downloads
-
- 28
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
A unified platform to manage, share, and archive morphological and functional data in insect neuroscience
eLife 10:e65376.
https://doi.org/10.7554/eLife.65376
Further reading
-
- Neuroscience
One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal least-action principle for cortical processing of sensory streams to produce appropriate behavioral outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimizes the local somato-dendritic mismatch error within individual neurons. For output neurons, the principle implies minimizing an instantaneous behavioral error. For deep network neurons, it implies the prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory input and the motor feedback during the ongoing sensory-motor transform. Online synaptic plasticity reduces the somatodendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic laws for global real-time computation and learning in the brain.
-
- Cell Biology
- Neuroscience
Sorting nexin 4 (SNX4) is an evolutionary conserved organizer of membrane recycling. In neurons, SNX4 accumulates in synapses, but how SNX4 affects synapse function remains unknown. We generated a conditional SNX4 knock-out mouse model and report that SNX4 cKO synapses show enhanced neurotransmission during train stimulation, while the first evoked EPSC was normal. SNX4 depletion did not affect vesicle recycling, basic autophagic flux, or the levels and localization of SNARE-protein VAMP2/synaptobrevin-2. However, SNX4 depletion affected synapse ultrastructure: an increase in docked synaptic vesicles at the active zone, while the overall vesicle number was normal, and a decreased active zone length. These effects together lead to a substantially increased density of docked vesicles per release site. In conclusion, SNX4 is a negative regulator of synaptic vesicle docking and release. These findings suggest a role for SNX4 in synaptic vesicle recruitment at the active zone.
