AxoSim model checkpoints

This repository is the model archive for AxoSim, a PyTorch package for scalable neuronal-surrogate simulation. It contains every retained AxoSim checkpoint family as of 2026-09-05. Rejected search trials, superseded periodic snapshots, optimizer/resume state, prediction caches, and third-party Branch-ELM weights are intentionally excluded.

Checkpoint groups

• released/: the original dual-objective, structured-compact, regression-specialized, spike-specialized, and development-population releases.
• ordinary/: the four final ordinary-AxoBench publication profiles selected at frozen 32M/40M presentation boundaries.
• population-finetuned/: the same four architecture profiles after the fixed 30K-presentation axobench-population specialization protocol.

manifest.json is authoritative: it records SHA-256, byte size, model class, parameter count, provenance, checkpoint metadata fields, and available frozen evaluation metrics for every file.

Loading

Use the AxoSim source revision compatible with these checkpoints:

git clone https://github.com/DavideWiest/axosim
cd axosim
pip install -e .

from axosim.checkpoint import load_checkpoint

model, metadata = load_checkpoint("ordinary/axosim-mamba-medium-40m.pt", map_location="cpu")
model.eval()

Released Mamba checkpoints require AxoSim's official fused Mamba backend. The checkpoint embeds its architecture configuration; the JSON files in configs/ are included for readable reference and new training runs.

Intended use and limits

These are research neuronal-surrogate checkpoints, not biological ground truth or clinical models. Ordinary and population-specialized scores use different evaluation distributions and should not be compared as if they were one test set. Consult the AxoSim repository and its report for input/output contracts, dataset roles, and evaluation details.

No standalone license was present in the AxoSim source repository at publication time. The files are published for research inspection and reproducibility; ask the AxoSim authors before redistribution or commercial use.