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Learning to construct sequential events

Code for the preprint 'Consolidation of sequential experience into a deep generative network explains human memory, prediction and planning'.

Installation

To install and run the code:

git clone https://github.com/ellie-as/sequence-memory.git
cd sequence-memory
pip install -r requirements.txt

Then open the relevant Jupyter notebook (see below).

The code was tested with Python 3.8.10, with trainng run on a single A100. To train models on MacOS, add --use_mps_device whenever the run_clm_*.py script is run.

Subfolders

  • narratives: code for showing how the model neocortex learns the gist of specific events in Bartlett (1932) and Raykov et al. (2023), reconstructing them with gist-based distortions.
  • statistical learning: code for Durrant et al. (2011) simulation.
  • inference: code for exploring relational inference in a spatial and family tree task based on Whittington et al. (2020), and for showing how RAG supports inference from recent memory.
  • planning: code for simulating the development of model-based planning abilities in Vikbladh et al. (2024) through consolidation.
  • misc: Code for representing memories as gists and details, and for sequence compression results in the SI.
  • hopfield: Code for simulations of asymmetric Hopfield networks in SI.
  • scripts: Helper scripts and utils, including the model training scripts (which are adapted from the HuggingFace Trasformers Python library training script examples).