A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System Article Swipe
Fabian Tschopp
,
Michael B. Reiser
,
Srinivas C. Turaga
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1806.04793
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1806.04793
What can we learn from a connectome? We constructed a simplified model of the first two stages of the fly visual system, the lamina and medulla. The resulting hexagonal lattice convolutional network was trained using backpropagation through time to perform object tracking in natural scene videos. Networks initialized with weights from connectome reconstructions automatically discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs, while networks initialized at random did not. Our work is the first demonstration, that knowledge of the connectome can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1806.04793
- https://arxiv.org/pdf/1806.04793
- OA Status
- green
- Cited By
- 18
- References
- 30
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2808029579
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2808029579Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1806.04793Digital Object Identifier
- Title
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A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual SystemWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-06-12Full publication date if available
- Authors
-
Fabian Tschopp, Michael B. Reiser, Srinivas C. TuragaList of authors in order
- Landing page
-
https://arxiv.org/abs/1806.04793Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1806.04793Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1806.04793Direct OA link when available
- Concepts
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Connectome, Computer science, Artificial intelligence, Lattice (music), Convolutional neural network, Orientation (vector space), Computer vision, Neuroscience, Physics, Mathematics, Biology, Functional connectivity, Geometry, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
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2025: 1, 2022: 3, 2021: 4, 2020: 7, 2019: 3Per-year citation counts (last 5 years)
- References (count)
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30Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tracking | 41 |
| abstract_inverted_index.direction | 58 |
| abstract_inverted_index.hexagonal | 28 |
| abstract_inverted_index.knowledge | 81 |
| abstract_inverted_index.resulting | 27 |
| abstract_inverted_index.structure | 108 |
| abstract_inverted_index.connectome | 51, 84 |
| abstract_inverted_index.discovered | 54 |
| abstract_inverted_index.functional | 92 |
| abstract_inverted_index.individual | 95 |
| abstract_inverted_index.properties | 60, 93 |
| abstract_inverted_index.simplified | 10 |
| abstract_inverted_index.well-known | 55 |
| abstract_inverted_index.connectome? | 6 |
| abstract_inverted_index.constructed | 8 |
| abstract_inverted_index.initialized | 47, 69 |
| abstract_inverted_index.orientation | 56 |
| abstract_inverted_index.predictions | 89 |
| abstract_inverted_index.selectivity | 59 |
| abstract_inverted_index.automatically | 53 |
| abstract_inverted_index.convolutional | 30 |
| abstract_inverted_index.understanding | 103 |
| abstract_inverted_index.demonstration, | 79 |
| abstract_inverted_index.backpropagation | 35 |
| abstract_inverted_index.reconstructions | 52 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |