reverse-pred 0.0.1

Library to run Reverse Predictivity

Reverse Predictivity

A research codebase accompanying the preprint:

Reverse Predictivity: Going Beyond One-Way Mapping to Compare Artificial Neural Network Models and Brains, Muzellec & Kar, bioRxiv (posted August 8, 2025) (biorxiv.org)

This repository supports analyses comparing macaque inferior temporal (IT) cortex responses with artificial neural network (ANN) units—specifically using a reverse predictivity metric that assesses how well neural responses predict ANN activations (biorxiv.org).

Compare brains and models in both directions.

This repository implements reverse predictivity: a complementary evaluation to forward neural predictivity that asks how well do neural responses predict ANN activations? It provides utilities to map macaque IT population responses to model units, quantify bidirectional alignment, and reproduce manuscript figures.

🧠 What is reverse predictivity?

Traditional forward neural predictivity evaluates how well a model’s features linearly predict neural responses. Reverse predictivity inverts that lens: using neural responses to predict model units. Agreement across both directions strengthens claims that a model and a brain area share representations. Practically, this repo includes:

• Regression utilities to decode IT neurons / ANN unit activations from ANN units / IT population responses
• Image‑level metrics and correlation suites to compare human/ANN/neural behaviors
• End‑to‑end notebooks to reproduce figures

🗂️ Repository layout

• demo_forward_predictivity.ipynb – quick demo of forward mapping model units -> neurons
• demo_reverse_predictivity.ipynb – quick demo of reverse mapping model units <- neurons
• demo_generate_neurons_i1.ipynb – compute image‑level neural metrics
• demo_generate_model_i1.ipynb – compute image‑level model metrics
• figure[1-6].ipynb – figure reproduction notebooks
• model_to_monkey.py – utilities for model -> neural regression and evaluation
• monkey_to_model.py – utilities for model <- neural regression and evaluation
• correlation_metrics.py – Spearman/Pearson, reliability‑aware correlations, confidence intervals
• regression_metrics.py – regression helpers
• prediction_utils.py – shared helpers for prediction/decoding
• decode_utils.py – train/test splits, cross‑validation, split‑half routines
• figure_utils.py – journal‑style plotting helpers
• h5_utils.py – helpers to read/write HDF5 feature and metadata files

📦 Large data files (IT features, image sets) are not stored in the repo. They can be downloaded from: here

🛠️ Installation

We recommend Python ≥3.10 with a fresh environment (Conda or venv).

# Using conda
conda create -n reverse_pred python=3.10 -y
conda activate reverse_pred

# Install core dependencies
pip install numpy scipy scikit-learn matplotlib h5py

📥 Data & preparation

This project assumes access to:

1. Macaque IT responses: population responses for N images.
   • /neural_data shape (n_images, n_neurons, n_reps)
2. Model features: precomputed ANN activations for the same images
   • /model_features shape (n_images, n_units)
3. Humans / Primates behavior: image‑level accuracies
   • /behavior shape (n_images)

🚀 Quickstart

• demo_forward_predictivity.ipynb – step‑by‑step guide to fitting a model to neuron regression, evaluating correlations.
• demo_reverse_predictivity.ipynb – end‑to‑end demonstration of neuron to model regression, computing EV/correlation metrics.
• demo_generate_neurons_i1.ipynb – generates image‑level accuracies from neural decoders.
• demo_generate_model_i1.ipynb – extracts image‑level model metrics from ANN activations.

🔁 Reproducing manuscript figures

Each figureX.ipynb notebook reproduces the corresponding figure from the preprint. Notebooks expect the data assets described above. If paths differ, change the config cell at the top of each notebook.

• Figure 1: Forward Predictivity
• Figure 2: Reverse vs forward predictivity examples
• Figure 3: Reverse vs forward predictivity accross monkeys and models
• Figure 4: Influencing factors
• Figure 5: Analysis of unique units
• Figure 6: Link with behavior

📌 Status & citation

This codebase accompanies the preprint:

Muzellec, S. & Kar, K. (2025). Reverse Predictivity: Going Beyond One‑Way Mapping to Compare Artificial Neural Network Models and Brains. bioRxiv.

If you use this repository or ideas from it, please cite the preprint and link to this repo.

@article{muzellec_kar_2025_reversepredictivity,
  title  = {Reverse Predictivity: Going Beyond One-Way Mapping to Compare Artificial Neural Network Models and Brains},
  author = {Muzellec, Sabine and Kar, Kohitij},
  year   = {2025},
  journal= {bioRxiv}
}

License: MIT (see LICENSE).