| ---
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| license: mit
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| library_name: pytorch
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| tags:
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| - spiking-neural-network
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| - neuroscience
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| - connectome
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| - drosophila
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| - olfaction
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| - norse
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| - biology
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| datasets:
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| - MIRE-org/door-olfactory-responses
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| pipeline_tag: tabular-classification
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| ---
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|
|
| # FlyWire Olfactory SNN (MaskedRecurrentLIFSNN)
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|
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| A **connectome-constrained recurrent spiking neural network** for odor identity
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| classification in *Drosophila melanogaster*, trained on the
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| [DoOR](https://github.com/ropensci/DoOR.data) olfactory receptor response dataset.
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|
|
| ## Model description
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|
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| The recurrent connectivity of this SNN is fixed to the **FlyWire** connectome
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| subgraph (antennal lobe projection neurons + mushroom body Kenyon cells). Synaptic
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| signs (excitatory/inhibitory) come from predicted neurotransmitter types in FlyWire.
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| Only the **weight magnitudes** are learned; the topology is biological.
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|
|
| ### Architecture
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|
|
| ```
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| Input: odor receptor vector (DoOR: ~52 receptors)
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| → Linear(input_dim → hidden_dim, no bias)
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| → 20 LIF timesteps with:
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| • Poisson spike encoding from rate-coded input
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| • Recurrent current: spk × (W_rec ⊙ mask ⊙ sign)ᵀ
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| • Norse LIFCell (surrogate gradient, α=100)
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| → time-averaged spike rates
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| → Linear(hidden_dim → num_classes)
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| ```
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|
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| - **Neuron model:** Leaky Integrate-and-Fire (Norse `LIFCell`, `method="super"`)
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| - **Recurrent mask:** Binary from FlyWire adjacency (fixed, not learned)
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| - **Synaptic signs:** ACh/DA/5-HT/OA → +1 (excitatory); GABA/Glu → −1 (inhibitory)
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| - **Training:** Adam optimizer, CrossEntropyLoss, surrogate gradients through LIF
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|
|
| ## Files
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|
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| | File | Description |
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| |------|-------------|
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| | `model.safetensors` | Trained weights (best validation checkpoint) |
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| | `config.json` | Architecture hyperparameters |
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| | `connectome_mask.npz` | FlyWire olfactory subgraph (binary adjacency + signs) |
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| | `connectome_meta.json` | Connectome metadata (neuron count, edge count, source) |
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| | `modeling_snn.py` | Standalone `MaskedRecurrentLIFSNN` class |
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|
|
| ## Usage
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|
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| ```python
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| import scipy.sparse as sp
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| import torch
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| from safetensors.torch import load_file
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|
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| # Load the model
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| from modeling_snn import MaskedRecurrentLIFSNN
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|
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| adjacency = sp.load_npz("connectome_mask.npz")
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| model = MaskedRecurrentLIFSNN(
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| input_dim=52, # from config.json
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| hidden_dim=800, # from config.json
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| num_classes=500, # from config.json
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| adjacency=adjacency,
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| steps=20,
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| alpha=100.0,
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| )
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| state_dict = load_file("model.safetensors")
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| model.load_state_dict(state_dict)
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| model.eval()
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|
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| # Inference
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| x = torch.randn(1, 52) # receptor activation vector
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| logits, spike_sparsity = model(x)
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| predicted_odor = logits.argmax(dim=1).item()
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| ```
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|
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| ## Training details
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|
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| - **Dataset:** DoOR (Database of Odorant Responses) — CC BY-SA 4.0
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| - **Cross-validation:** 5-fold over odor identities × 5 seeds = 25 runs
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| - **Early stopping:** patience 5 on validation accuracy
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| - **Optimizer:** Adam (lr=1e-3, weight_decay=1e-5)
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| - **Batch size:** 32
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| - **Max epochs:** 80
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| - **SNN timesteps:** 20
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| - **Evaluation:** 5× Monte Carlo averaging over stochastic Poisson encoding
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|
|
| ## Connectome-Constrained Spiking Neural Networks Olfactory Classification Study
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| This model was used in a classification study and ran against a comparable but shuffled spiking neural network Sparse MLP, and Dense MLP models.
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|
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| Summary Results may be found here: https://mire-institute.org/research-papers/connectomeconstrained-spiking-neural-networks-olfactory-classification-study
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|
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| And the full research paper may be found here: https://mire-institute.org/research-papers/connectomeconstrained-spiking-neural-networks-olfactory-classification-study-preprint
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|
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| ## Biological basis
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|
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| The model's recurrent topology is extracted from the [FlyWire](https://flywire.ai/)
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| whole-brain connectome of *Drosophila melanogaster* (FAFB dataset). The olfactory
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| subgraph includes:
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|
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| - **Antennal Lobe Projection Neurons (ALPN):** relay processed odor information
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| - **Kenyon Cells (KC):** mushroom body neurons for associative olfactory memory
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|
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| This captures the AL → PN → KC pathway that the fly uses for odor discrimination
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| and learning.
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|
|
| ## Citation
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|
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| If you use this model, please cite:
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|
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| - The **DoOR** database: Münch & Galizia (2016). DoOR 2.0 — Comprehensive mapping
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| of *Drosophila melanogaster* odorant responses. *Scientific Reports*, 6, 21841.
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| https://doi.org/10.1038/srep21841
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| - The **FlyWire** connectome: Dorkenwald et al. (2024). Neuronal wiring diagram of
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| an adult brain. *Nature*, 634, 124–138. https://doi.org/10.1038/s41586-024-07558-y
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| |
