Discretization of continuous input spaces in the hippocampal autoencoder

Adrian F. Amil, Ismael T. Freire, Paul F. M. J. Verschure
5/23/2024
cs.AIcs.LGq-bio.NC

No official code found. Best implementation: adriamilcar/HashingBrain (3 stars, last commit 2024-05-24).

Abstract

The hippocampus has been associated with both spatial cognition and episodic memory formation, but integrating these functions into a unified framework remains challenging. Here, we demonstrate that forming discrete memories of visual events in sparse autoencoder neurons can produce spatial tuning similar to hippocampal place cells. We then show that the resulting very high-dimensional code enables neurons to discretize and tile the underlying image space with minimal overlap. Additionally, we extend our results to the auditory domain, showing that neurons similarly tile the frequency space in an experience-dependent manner. Lastly, we show that reinforcement learning agents can effectively perform various visuo-spatial cognitive tasks using these sparse, very high-dimensional representations.

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Statistics of spatially-tuned activations after dimensionality reduction of images.

30Python, Jupyter NotebookMar 8, 20232 years ago

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Cite this paper

@article{amil2024discretization,
  title  = {Discretization of continuous input spaces in the hippocampal autoencoder},
  author = {Adrian F. Amil and Ismael T. Freire and Paul F. M. J. Verschure},
  year   = {2024},
  eprint = {2405.14600},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2405.14600v1}
}

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