Multilevel visual motion opponency in Drosophila
Open accessAbstract
Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Inhibition can be shunting rather than subtractive in some fly neurons, and the conductance of an inhibitory synapse can be ten times higher than that of an excitatory synapse.
“For example, inhibition can be shunting rather than subtractive in some fly neurons100, and the conductance of an inhibitory synapse can be ten times higher than that of an excitatory synapse101.”
From Neuronal wiring diagram of an adult brain (2024) · cites this paper as [101]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368 · see citing works
Spatiotemporal energy models for the perception of motion
Adelson, E. H., & Bergen, J. R. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.000284 · Cited by 3,442 · see citing works
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906 · see citing works
A connectome and analysis of the adult Drosophila central brain
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-Y., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., et al. (2020) · eLife · doi:10.7554/elife.57443 · Cited by 1,128 · cited 3× · see citing works
“We thus asked whether such a connectivity pattern is apparent in the hemibrain connectome 26 (Fig.”
BackgroundMethodSurvey of spiking in the mouse visual system reveals functional hierarchy
Siegle, J. H., Jia, X., Durand, S., Gale, S. D., Bennett, C., Graddis, N., Heller, G. R., Ramirez, T. K., Choi, H., Luviano, J., et al. (2021) · Nature · doi:10.1038/s41586-020-03171-x · Cited by 610 · see citing works
From the connectome to brain function
Bargmann, C. I., & Marder, E. (2013) · Nature Methods · doi:10.1038/nmeth.2451 · Cited by 561 · see citing works
Inhibition dominates sensory responses in the awake cortex
Haider, B., Häusser, M., & Carandini, M. (2012) · Nature · doi:10.1038/nature11665 · Cited by 541 · see citing works
The response of area MT and V1 neurons to transparent motion
Snowden, R., Treue, S., Erickson, R. G., & Andersen, R. A. (1991) · Journal of Neuroscience · doi:10.1523/jneurosci.11-09-02768.1991 · Cited by 457 · see citing works
The connectome of an insect brain
Winding, M., Pedigo, B. D., Barnes, C. L., Patsolic, H. G., Park, Y., Kazimiers, T., Fushiki, A., Andrade, I. V., Khandelwal, A., Valdes-Aleman, J., et al. (2023) · Science · doi:10.1126/science.add9330 · Cited by 435 · see citing works
Active flight increases the gain of visual motion processing in Drosophila
Maimon, G., Straw, A. D., & Dickinson, M. H. (2010) · Nature Neuroscience · doi:10.1038/nn.2492 · Cited by 415 · see citing works
Estimation of self-motion by optic flow processing in single visual interneurons
Krapp, H. G., & Hengstenberg, R. (1996) · Nature · doi:10.1038/384463a0 · Cited by 409 · see citing works
A directional tuning map of Drosophila elementary motion detectors
Maisak, M. S., Haag, J., Ammer, G., Serbe, E., Meier, M., Leonhardt, A., Schilling, T., Bahl, A., Rubin, G. M., Nern, A., et al. (2013) · Nature · doi:10.1038/nature12320 · Cited by 390 · see citing works
Self-citationArchitectures of neuronal circuits
Luo, L. (2021) · Science · doi:10.1126/science.abg7285 · Cited by 390 · see citing works
Krapp, H. G., Hengstenberg, B., & Hengstenberg, R. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.4.1902 · Cited by 303 · see citing works
Positive and Negative Circuits in Dynamical Systems
Gouzé, J. (1998) · Journal of Biological Systems · doi:10.1142/s0218339098000054 · Cited by 273 · see citing works
A modular display system for insect behavioral neuroscience
Reiser, M. B., & Dickinson, M. H. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.07.019 · Cited by 272 · see citing works
Gegenfurtner, K. R., & Kiper, D. (2003) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131116 · Cited by 272 · see citing works
Transparent motion perception as detection of unbalanced motion signals. I. Psychophysics
Ning Qian, A., Andersen, R. A., & Adelson, E. H. (1994) · Journal of Neuroscience · doi:10.1523/jneurosci.14-12-07357.1994 · Cited by 270 · see citing works
A genetic, genomic, and computational resource for exploring neural circuit function
Davis, F. P., Nern, A., Picard, S., Reiser, M. B., Rubin, G. M., Eddy, S. R., & Henry, G. L. (2020) · eLife · doi:10.7554/elife.50901 · Cited by 268 · see citing works
Genetically Targeted Optical Electrophysiology in Intact Neural Circuits
Cao, G., Platiša, J., Pieribone, V. A., Raccuglia, D., Kunst, M., & Nitabach, M. N. (2013) · Cell · doi:10.1016/j.cell.2013.07.027 · Cited by 262 · see citing works
Subcellular Imaging of Voltage and Calcium Signals Reveals Neural Processing In Vivo
Yang, H. H., St-Pierre, F., Sun, X., Ding, X., Lin, M. Z., & Clandinin, T. R. (2016) · Cell · doi:10.1016/j.cell.2016.05.031 · Cited by 253 · see citing works
Connectomic comparison of mouse and human cortex
Loomba, S., Straehle, J., Gangadharan, V., Heike, N., Khalifa, A., Motta, A., Ju, N., Sievers, M., Gempt, J., Meyer, H. S., et al. (2022) · Science · doi:10.1126/science.abo0924 · Cited by 242 · see citing works
Necessary Conditions for Multistationarity and Stable Periodicity
Snoussi, E. H. (1998) · Journal of Biological Systems · doi:10.1142/s0218339098000042 · Cited by 239 · see citing works
The comprehensive connectome of a neural substrate for ‘ON’ motion detection in Drosophila
Takemura, S.-Y., Nern, A., Chklovskii, D. B., Scheffer, L. K., Rubin, G. M., & Meinertzhagen, I. A. (2017) · eLife · doi:10.7554/elife.24394 · Cited by 210 · see citing works
Joesch, M., Plett, J., Borst, A., & Reiff, D. F. (2008) · Current Biology · doi:10.1016/j.cub.2008.02.022 · Cited by 197 · see citing works
Self-citationUltra-selective looming detection from radial motion opponency
Klapoetke, N. C., Nern, A., Peek, M. Y., Rogers, E. M., Breads, P., Rubin, G. M., Reiser, M. B., & Card, G. M. (2017) · Nature · doi:10.1038/nature24626 · Cited by 173 · see citing works
neuPrint: an open access tool for EM connectomics
Plaza, S. M., Clements, J., Dolafi, T., Umayam, L., Neubarth, N. N., Scheffer, L. K., & Berg, S. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.896292 · Cited by 145 · see citing works
Comparisons between the ON- and OFF-edge motion pathways in the Drosophila brain
Shinomiya, K., Huang, G., Lu, Z., Parag, T., Xu, C. S., Aniceto, R., Ansari, N., Cheatham, N., Lauchie, S., Neace, E., et al. (2019) · eLife · doi:10.7554/elife.40025 · Cited by 140 · see citing works
Quantitative Predictions Orchestrate Visual Signaling in Drosophila
Kim, A. J., Fenk, L. M., Lyu, C., & Maimon, G. (2017) · Cell · doi:10.1016/j.cell.2016.12.005 · Cited by 139 · see citing works
Arenz, A., Drews, M. S., Richter, F. G., Ammer, G., & Borst, A. (2017) · Current Biology · doi:10.1016/j.cub.2017.01.051 · Cited by 137 · see citing works
Self-citationNeural Circuit to Integrate Opposing Motions in the Visual Field
Mauss, A. S., Pankova, K., Arenz, A., Nern, A., Rubin, G. M., & Borst, A. (2015) · Cell · doi:10.1016/j.cell.2015.06.035 · Cited by 132 · see citing works
Self-citationColor Processing in the Early Visual System of Drosophila
Schnaitmann, C., Haikala, V., Abraham, E., Oberhauser, V., Thestrup, T., Griesbeck, O., & Reiff, D. F. (2018) · Cell · doi:10.1016/j.cell.2017.12.018 · Cited by 129 · see citing works
Complementary mechanisms create direction selectivity in the fly
Haag, J., Arenz, A., Serbe-Kamp, E., Gabbiani, F., & Borst, A. (2016) · eLife · doi:10.7554/elife.17421 · Cited by 104 · see citing works
Self-citationA faithful internal representation of walking movements in the Drosophila visual system
Fujiwara, T., Cruz, T. L., Bohnslav, J. P., & Chiappe, M. E. (2016) · Nature Neuroscience · doi:10.1038/nn.4435 · Cited by 102 · see citing works
Flies and humans share a motion estimation strategy that exploits natural scene statistics
Clark, D. A., Fitzgerald, J. E., Ales, J. M., Gohl, D. M., Silies, M., Norcia, A. M., & Clandinin, T. R. (2014) · Nature Neuroscience · doi:10.1038/nn.3600 · Cited by 94 · see citing works
A biophysical account of multiplication by a single neuron
Groschner, L. N., Malis, J. G., Zuidinga, B., & Borst, A. (2022) · Nature · doi:10.1038/s41586-022-04428-3 · Cited by 94 · see citing works
Self-citationOptogenetic and Pharmacologic Dissection of Feedforward Inhibition inDrosophilaMotion Vision
Mauss, A. S., Meier, M., Serbe-Kamp, E., & Borst, A. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3938-13.2014 · Cited by 92 · see citing works
Self-citationLeong, J. C. S., Esch, J. J., Poole, B., Ganguli, S., & Clandinin, T. R. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1272-16.2016 · Cited by 90 · see citing works
Introduction to Synaptic Circuits
Shepherd, G. M. (2004) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195159561.003.0001 · Cited by 88 · see citing works
Synaptic wiring motifs in posterior parietal cortex support decision-making
Kuan, A. T., Bondanelli, G., Driscoll, L. N., Han, J., Kim, M., Hildebrand, D. G. C., Graham, B. J., Wilson, D. E., Thomas, L. A., Panzeri, S., et al. (2024) · Nature · doi:10.1038/s41586-024-07088-7 · Cited by 78 · see citing works
Binocular, Directionally Selective Neurones, Possibly Involved in the Optomotor Response of Insects
Collett, T. S., & Blest, A. D. (1966) · Nature · doi:10.1038/2121330a0 · Cited by 74 · see citing works
Mysore, S. P., & Knudsen, E. I. (2012) · Neuron · doi:10.1016/j.neuron.2011.10.037 · Cited by 71 · see citing works
Koyama, M., Minale, F., Shum, J. W. H., Nishimura, N., Schaffer, C. B., & Fetcho, J. R. (2016) · eLife · doi:10.7554/elife.16808 · Cited by 69 · see citing works
Dynamic Signal Compression for Robust Motion Vision in Flies
Drews, M. S., Leonhardt, A., Pirogova, N., Richter, F. G., Schützenberger, A., Braun, L., Serbe-Kamp, E., & Borst, A. (2020) · Current Biology · doi:10.1016/j.cub.2019.10.035 · Cited by 65 · see citing works
Self-citation<i>neu</i> Print: Analysis Tools for EM Connectomics
Clements, J., Dolafi, T., Umayam, L., Neubarth, N. L., Berg, S., Scheffer, L. K., & Plaza, S. M. (2020) · doi:10.1101/2020.01.16.909465 · Cited by 65 · see citing works
Neuronal circuits integrating visual motion information in Drosophila melanogaster
Shinomiya, K., Nern, A., Meinertzhagen, I. A., Plaza, S. M., & Reiser, M. B. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.061 · Cited by 64 · see citing works
A minimal synaptic model for direction selective neurons in Drosophila
Zavatone-Veth, J. A., Badwan, B. A., & Clark, D. A. (2020) · Journal of Vision · doi:10.1167/jov.20.2.2 · Cited by 62 · see citing works
Spatial readout of visual looming in the central brain of Drosophila
Morimoto, M. M., Nern, A., Zhao, A., Rogers, E. M., Wong, A. M., Isaacson, M. D., Bock, D. D., Rubin, G. M., & Reiser, M. B. (2020) · eLife · doi:10.7554/elife.57685 · Cited by 56 · see citing works
Optic flow-based course control in insects
Mauss, A. S., & Borst, A. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.10.007 · Cited by 54 · see citing works
Self-citationFendl, S., Vieira, R. M., & Borst, A. (2020) · eLife · doi:10.7554/elife.62953 · Cited by 48 · see citing works
Self-citationBi-directional Control of Walking Behavior by Horizontal Optic Flow Sensors
Busch, C., Borst, A., & Mauss, A. S. (2018) · Current Biology · doi:10.1016/j.cub.2018.11.010 · Cited by 42 · see citing works
Self-citationConnectomic features underlying diverse synaptic connection strengths and subcellular computation
Liu, T. X., Davoudian, P. A., Lizbinski, K. M., & Jeanne, J. M. (2022) · Current Biology · doi:10.1016/j.cub.2021.11.056 · Cited by 39 · see citing works
Dynamic nonlinearities enable direction opponency in Drosophila elementary motion detectors
Badwan, B. A., Creamer, M. S., Zavatone-Veth, J. A., & Clark, D. A. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0443-y · Cited by 36 · see citing works
A biophysical mechanism for preferred direction enhancement in fly motion vision
Borst, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006240 · Cited by 35 · see citing works
Self-citationA competitive disinhibitory network for robust optic flow processing in Drosophila
Erginkaya, M., Cruz, T. L., Brotas, M., Marques, A., Steck, K., Nern, A., Torrão, F., Varela, N., Bock, D. D., Reiser, M. B., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01948-9 · Cited by 29 · see citing works
Small-field visual projection neurons detect translational optic flow and support walking controlPreprint
Isaacson, M. D., Eliason, J. L. M., Nern, A., Rogers, E. M., Lott, G. K., Tabachnik, T., Rowell, W. J., Edwards, A. W., Korff, W. L., Rubin, G. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.21.546024 · Cited by 28 · see citing works
Neural mechanism of spatio-chromatic opponency in the Drosophila amacrine neurons
Li, Y., Chen, P., Lin, T., Ting, C., Muthuirulan, P., Pursley, R. H., Ilić, M., Pirih, P., Drews, M. S., Menon, K. P., et al. (2021) · Current Biology · doi:10.1016/j.cub.2021.04.068 · Cited by 26 · see citing works
Self-citationGruntman, E., Reimers, P., Romani, S., & Reiser, M. B. (2021) · Current Biology · doi:10.1016/j.cub.2021.09.072 · Cited by 13 · see citing works
A competitive disinhibitory network for robust optic flow processing in DrosophilaPreprint
Erginkaya, M., Cruz, T. L., Brotas, M., Steck, K., Nern, A., Torrão, F., Varela, N., Bock, D. D., Reiser, M. B., & Chiappe, M. E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.06.552150 · Cited by 12 · see citing works
Electrophysiological Recordings from Lobula Plate Tangential Cells in Drosophila
Mauss, A. S., & Borst, A. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6371-3_20 · Cited by 10 · see citing works
Self-citationData and code for 'Multi-level visual motion opponency in Drosophila'
Ammer, G., Serbe-Kamp, E., Mauss, A. S., Richter, F. G., Fendl, S., & Borst, A. (2023) · German Neuroinformatics Node · doi:10.12751/g-node.7v3pe6 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [4]Shepherd, G. M. in The Synaptic Organization of the Brain (ed. Shepherd, G. M.) 1–38 (Oxford Univ. Press, 2004).
- [41]Koch, C. Biophysics of Computation: Information Processing in Single Neurons (Oxford Univ. Press, 2004).
Cited by
Sorted by citations
Neuronal wiring diagram of an adult brain
Dorkenwald, S., Matsliah, A., Sterling, A. R., Schlegel, P., Yu, S.-C., McKellar, C. E., Lin, A., Costa, M., Eichler, K., Yin, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07558-y · Cited by 570 · see citing works
“For example, inhibition can be shunting rather than subtractive in some fly neurons 100 , and the conductance of an inhibitory synapse can be ten times higher than that of an excitatory synapse 101 .”
BackgroundNeuronal parts list and wiring diagram for a visual system
Matsliah, A., Yu, S.-C., Kruk, K., Bland, D., Burke, A. T., Gager, J., Hebditch, J., Silverman, B., Willie, K. P., Willie, R., et al. (2024) · Nature · doi:10.1038/s41586-024-07981-1 · Cited by 85 · see citing works
Self-citationPredicting visual function by interpreting a neuronal wiring diagram
Seung, H. S. (2024) · Nature · doi:10.1038/s41586-024-07953-5 · Cited by 34 · see citing works
Neuronal “parts list” and wiring diagram for a visual systemPreprint
Matsliah, A., Yu, S.-C., Kruk, K., Bland, D., Burke, A., Gager, J., Hebditch, J., Silverman, B., Willie, K. P., Willie, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.12.562119 · Cited by 21 · see citing works
Biased cell adhesion organizes the Drosophila visual motion integration circuit
Carrier, Y., Rio, L. Q., Formicola, N., de Sousa-Xavier, V., Tabet, M., Chen, Y., Ali, A. H., Wislez, M., Orts, L., Borst, A., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.10.019 · Cited by 11 · see citing works
Self-citationA competitive disinhibitory network for robust optic flow processing in Drosophila
Erginkaya, M., Cruz, T. L., Brotas, M., Marques, A., Steck, K., Nern, A., Torrão, F., Varela, N., Bock, D. D., Reiser, M. B., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01948-9 · Cited by 29 · see citing works
Interneuron diversity and normalization specificity in a visual systemPreprint
Seung, H. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.03.587837 · Cited by 13 · see citing works
Boulanger-Weill, J., Kämpf, F., Schuhknecht, G. F. P., Schalek, R. L., Garza, R., Petkova, M. D., Vohra, S. K., Wu, Y., Savaliya, J. H., Tiller, R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.14.643363 · Cited by 8 · see citing works
On analogies in vertebrate and insect visual systems
Tanaka, R., & Portugues, R. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00932-3 · Cited by 5 · see citing works
The Synaptic Complexity of a High‐Integration Lobula Giant Neuron in Crabs
Barnatan, Y., Rind, C., Scarano, F., & Sztarker, J. (2025) · The Journal of comparative neurology · doi:10.1002/cne.70026
Convergence-divergence circuits for multimodal integration of innate and learned opponent valences
Wang, W., Yang, Y., Liu, Q., Gao, Y. N., Lv, Q., Zhang, K., Ning, J., & Sun, Y. (2026) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2026.1822122 · Cited by 1 · see citing works
Inhibitory columnar feedback neurons are involved in motion processing in DrosophilaPreprint
Henning, M., Ketkar, M. D., Lüffe, T., Gohl, D. M., Clandinin, T. R., & Silies, M. (2025) · eLife · doi:10.7554/elife.108529.3 · Cited by 2 · see citing works
Connectome analysis reveals brainwide visual processing in Drosophila
Kim, S. Y., & Kim, A. J. (2026) · bioRxiv · doi:10.64898/2026.02.02.700492 · Cited by 1 · see citing works
Inhibitory columnar feedback neurons are involved in motion processing in DrosophilaPreprint
Henning, M., Ketkar, M. D., Lüffe, T., Gohl, D. M., Clandinin, T. R., & Silies, M. (2026) · eLife · doi:10.7554/elife.108529.2 · Cited by 1 · see citing works
Vectorial efference copy and visuomotor transformation through gap junctionsPreprint
Falt, T., Ammer, G., Serbe-Kamp, E., Kroell, L. M., Friedrich, A. B., Mota, S. G., Simsova, E., & Fenk, L. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.11.744090 · Cited by 2 · see citing works
Self-citationGaze Control in Drosophila During Pursuit and Navigation
Chiappe, M. E., & Fenk, L. M. (2026) · Annual Review of Vision Science · doi:10.1146/annurev-vision-101322-105900
Connectomes: a re-emerging hub dataset for neuroscience
Kyllmann, S., & Keesey, I. W. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1911198 · Cited by 1 · see citing works
- Authors
- Ammer, G., Serbe-Kamp, E., Mauss, A. S., Richter, F. G., Fendl, S., & Borst, A.
- Affiliations
- Max Planck Institute for Biological Intelligence (DE)
- Corresponding author
- Ammer, Georg
- Venue
- Nature Neuroscience
- Published
- 2023-10-02
- Volume
- 26
- Issue
- 11
- Pages
- 1894-1905
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC10620086
- OpenAlex
- W4387252283
- Cited by
- 17
- References
- 59
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 5 · 2023: 1
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Max-Planck-Gesellschaft
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurobiology and Insect Physiology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- visual motion processing, direction selectivity, Drosophila visual system, inhibitory interneurons, noise robustness
- MeSH terms
- Animals, Drosophila, Motion Perception, Neurons, Synapses, Visual Pathways
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review