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arXiv:1803.07770q-bio.NC

Emergence of grid-like representations by training recurrent neural networks to perform spatial localization

Christopher J. Cueva, Xue-Xin Wei

Abstract

Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.

arXiv comments
Journal reference
International Conference on Learning Representations (ICLR) 2018
DOI
Subjects
q-bio.NCNeurons and Cognition cs.AIArtificial Intelligence cs.NENeural and Evolutionary Computing stat.MLMachine Learning (Statistics)

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Linked by 4 identifiable commenters in 5 comments across 5 threads, from May 2018 to Jun 2024.

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Discussed in comments5 threads

  1. A most profound video game: a good cognitive aid for research 1 comment

    • You might enjoy this paper[0] which shows that recurrent position encodings recover grid cell representations and maps to path integration found in a popular model of the hippocampus. This isn't terribly surprising since RNNs have shown this before[1, 2] but its an interesting…

      soraki_soladeadView on HNParent context

  2. Scientists discover specific neurons involved in making memories 1 comment

    • Neuroscience is not my specialty, I only understand the ML side of it. My understanding is that when our machine learning architectures are specially crafted to model specific types of data, we observe similar neurons as those in the brain because they converge to the same…

      ad404b8a372f2b9View on HNParent context

  3. The Brain’s “Inner GPS” Creates Conceptual Spaces in the Mind 1 comment

    • In addition, multiple works[0][1] have discovered that grid cell representations arise from regularized recurrent networks when provided relative inputs to predict absolute outputs. [0] https://arxiv.org/abs/1803.07770 [1] https://deepmind.com/blog/grid-cells/

      jimflemingView on HNParent context

  4. Grid-Cell Like Deep Layers in Navigation AI 1 comment

    • This DeepMind work has been gathering a lot of hype, but it is very similar to this work by Cueva and Wei: Published on Arxiv on March 31: https://arxiv.org/abs/1803.07770 Published at ICLR 2018, a well-known machine learning conference: https://openreview.net/forum?id=B17JTOe0…

      baylearnView on HNParent context

  5. Artificial Neural Nets Grow Brainlike Navigation Cells 1 comment

    • I think you're referring to this paper: "Emergence of grid-like representations by training recurrent neural networks to perform spatial localization". https://arxiv.org/abs/1803.07770 It appears to be from Columbia vs DeepMind with different authors.

      jimflemingView on HNParent context

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