Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Connectome-Based Modelling Reveals Orientation Maps in the Drosophila Optic Lobe
Authors: Jia Nuo Liew, Shenghan Lin, Bowen Chen, Wei Zhang, Xiaowei Zhu, Xiaolin Hu
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We integrate a complete fruit fly brain connectome with biologically grounded spiking neuron models to simulate neuroprocessing in the fly visual system. By driving the network with oriented stimuli and analysing downstream responses, we show that coherent orientation maps can emerge from purely connectome-constrained dynamics. These results suggest that species of independent origin could evolve similar visual structures. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing 100084, China 2IDG/Mc Govern Institute of Brain Research, Tsinghua University, Beijing 100084, China 3Zhili College, Tsinghua University, Beijing 100084, China 4School of Life Sciences, Tsinghua University, Beijing 100084, China 5Ant Group, China 6State Key Laboratory of Membrane Biology, Tsinghua University, Beijing 100084, China 7Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing 100084, China 8Chinese Institute for Brain Research (CIBR), Beijing 100010, China |
| Pseudocode | No | The paper describes methods through narrative text, mathematical formulas, and diagrams but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The full code and data can be found at https://github.com/JNLiew/flylif_orientation_maps. |
| Open Datasets | Yes | Using the full adult fly brain (FAFB) connectome, we constructed a network in which neurons are labelled and connected according to their anatomical synapses [8, 13, 20, 50]. The FAFB connectome provides a complete and cell-resolved reconstruction of the Drosophila brain [50], and has become a foundational resource for structural annotation [44], functional inference [49, 11], and whole-brain spiking simulations validated against behaviour [46]. |
| Dataset Splits | No | The paper describes a simulation study using a connectome, generating stimuli and analyzing responses, but does not involve explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | The model ran on an Intel(R) CPU at 2.90GHz machine using 20 threads in parallel. |
| Software Dependencies | No | The paper mentions implementing a conductance-based leaky integrate-and-fire (LIF) model and using the scipy Python library but does not provide specific version numbers for these software components or the programming language used. |
| Experiment Setup | Yes | The model used the following parameter values obtained from Drosophila modelling or electrophysiology efforts [9, 26, 28, 42, 46]: Vresting = 52 mV: resting potential [28, 46] Vreset = 52 mV: reset potential [28, 46] Vthreshold = 45 mV: spiking threshold [28, 46] Rmbr = 10 kโฆcm2: membrane resistance [28, 46] Cmbr = 2 ยตF cm 2: membrane capacitance [28, 46] Tmbr = Rmbr Cmbr: membrane time constant [46] Trefractory = 2.2 ms: refractory period [28, 31, 46] ฯ = 5 ms: synaptic decay time constant [26, 46] Tdly = 1.8 ms: synaptic transmission delay [46] wsyn = 1.5 mV: synaptic weight; free parameter. |