A connectome-constrained deep mechanistic network (DMN) model of the fruit fly visual system in PyTorch.
- Explore connectome-constrained models of the fruit fly visual system.
- Generate and test hypotheses about neural computations.
- Try pretrained models on your data.
- Develop custom models using our framework.
Flyvis is our official implementation of Lappalainen et al., "Connectome-constrained networks predict neural activity across the fly visual system." Nature (2024).
For detailed documentation, installation instructions, tutorials, and API reference, visit our documentation website.
Explore our tutorials to get started with flyvis. You can run them locally or try them for a quick start in Google Colab:
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Explore the Connectome
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Train the Network on the Optic Flow Task
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Flash Responses
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Moving Edge Responses
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Ensemble Clustering
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Maximally Excitatory Stimuli
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Custom Stimuli
Note: Some Google Colab links are still being prepared and will be added soon.
- Fig. 1: Connectome-constrained and task-optimized models of the fly visual system
- Fig. 2: Ensembles of DMNs predict tuning properties
- Fig. 3: Cluster analysis of DMN ensembles enables hypothesis generation and suggests experimental tests
- Fig. 4: Task-optimal DMNs largely recapitulate known mechanisms of motion computation
@article{lappalainen2024connectome,
title = {Connectome-constrained networks predict neural activity across the fly visual system},
issn = {1476-4687},
url = {https://doi.org/10.1038/s41586-024-07939-3},
doi = {10.1038/s41586-024-07939-3},
journal = {Nature},
author = {Lappalainen, Janne K. and Tschopp, Fabian D. and Prakhya, Sridhama and McGill, Mason and Nern, Aljoscha and Shinomiya, Kazunori and Takemura, Shin-ya and Gruntman, Eyal and Macke, Jakob H. and Turaga, Srinivas C.},
month = sep,
year = {2024},
}
For questions or inquiries, please contact us.