Code & analysis notebooks for Zhang et al., 2025
This repository contains the analysis pipelines used in the Zhang et al. 2025 study investigating how internal state (hunger, satiation, threat, safety) shapes population‑level neural dynamics in prelimbic cortex and behavior in rats. The code is organised as a set of Jupyter notebooks that:
- quantify cue‑evoked responses across states
- train CEBRA behaviour‑based embeddings
- perform spectral clustering analyses
- generate figure panel included in the paper.
All workflows run in pure Python.
| Path | Purpose |
|---|---|
Behavior/ |
Ethogram construction, behavioural metrics, and statistical analysis notebooks. Related to Fig. 1 and Extended Data Fig. 1. |
CEBRA/ |
Training & visualisation of CEBRA embeddings. Related to Fig. 2 and Extended Data Fig. 3. |
Cue responses/ |
State‑specific cue response quantification and decoding analyses. Related to Fig. 3 and Extended Data Fig. 4-5. |
Spectral Clustering/ |
Unsupervised clustering of calcium imaging and single‑unit peri‑event activity. Related to Fig. 4-5 and Extended Data Fig. 6-10. |
Cross_regist/ |
Cross‑session cell‑identity registration and footprint visualisation. Related to Fig. 4. |
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CEBRA notebooks – see https://cebra.ai for up‑to‑date environment requirements.
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Other notebooks – tested with python==3.11 and scikit‑learn==1.2.2.
Last updated: 2 July 2025.