Simulation code

.github/workflows/checks.yaml

@@ -0,0 +1,10 @@
+name: Checks
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+      - uses: pre-commit/action@v3.0.0

.pre-commit-config.yaml

@@ -0,0 +1,22 @@
+repos:
+  - repo: https://github.com/psf/black
+    rev: 22.6.0
+    hooks:
+      - id: black
+        language_version: python3
+        args: ["--line-length", "120"]
+        exclude: '\.(csv|yml|yaml)$'
+  # - repo: https://github.com/pycqa/flake8
+  #  rev: 4.0.1
+  #  hooks:
+  #    - id: flake8
+  #      args: ["--max-line-length", "120", "--ignore", "E203"]
+  #      language_version: python3
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: requirements-txt-fixer
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+        exclude: '\.(csv|yml|yaml)$'

README.md

@@ -1,2 +1,62 @@
-# sapinet_regularization
-Reproduction of simulations from Moyal et al. (202x) _Heterogeneous quantization regularizes spiking neural network activity_.
+# Heterogeneous Quantization Regularizes Spiking Neural Network Activity
+
+Spiking neural network simulations from [Moyal et al.](
+https://www.nature.com/articles/s41598-025-96223-z) (2025), tested on Ubuntu 22.04.
+
+
+Installation
+------------
+If you would like to run, modify, or extend this project:
+
+* Clone the repository:
+
+      git clone https://github.com/cplab/sapinet_regularization
+
+* Create a conda virtual environment (optional):
+
+      conda create -n <env_name> python=3.11
+      conda activate <env_name>
+
+* Install with pip:
+
+      cd sapinet_regularization
+      pip install -e .
+
+See ``setup.py`` for dependency information.
+
+
+Simulation
+----------
+* To reproduce the results and plots, run the master script from its own directory:
+
+      cd src/projects
+      python simulation.py -multirun yaml/hq2025.yaml
+
+* You may modify component, model, and project YAMLs to your liking and run single-use pipelines, e.g.:
+
+      cd src/projects
+      python simulation.py -experiment yaml/synthetic.yaml
+
+Plots and diagnostic output will be written to `results/<run>`.
+
+
+Analysis
+--------
+To reproduce the statistical analyses:
+
+* Edit the following R file so that `run_dir` and `meta_dir` point to the run directory
+generated in the previous step:
+
+      src/utils/analysis/analysis.r
+
+* Run `analysis.r` and inspect the results generated under `analysis/<run>`.
+
+You may also directly inspect the raw synthetic datasets and statistical output
+reported in the manuscript. Both are included in this repository under:
+
+      analysis/SciRep-Final
+
+
+Note
+----
+Sapicore 0.4 is compatible with both Linux and Windows.

analysis/SciRep-Final/csv/full_clf_cond_1.csv

@@ -0,0 +1,16 @@
+model.term,df1,df2,F.ratio,p.value
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+Layer:Session,1,441,0.042,0.838225874225619
+Condition:Duplication,9,441,28.307,9.3891107570878e-39
+Condition:Session,3,441,937.419,6.69716854927288e-191
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+Layer:Condition:Session,3,441,1.498,0.21432258197009
+Layer:Duplication:Session,3,441,0.047,0.986298344329484
+Condition:Duplication:Session,9,441,7.022,1.65477356379195e-09
+Layer:Condition:Duplication:Session,9,441,2.719,0.00429506307005925

analysis/SciRep-Final/csv/full_clf_cond_2.csv

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analysis/SciRep-Final/csv/full_clf_cond_3.csv

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analysis/SciRep-Final/csv/full_clf_dup_1.csv

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analysis/SciRep-Final/csv/full_clf_dup_2.csv

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