Decoding Grid Cells using Machine Learning

Hi all. I want to share what I’ve been doing in the past few days.

So, Numenta have released their grid cell paper for quite a while now. From the paper we know that grid cells can represent a very large space using relatively few bits. However, we don’t have a better way to decode grid cells besides using the SDR Classifer. Which doesn’t make sense (and is slow) since the representation of each location is unique thus a 1 to 1 mapping should be achievable. I have been thinking about algorithm to generate a probability field out of grid cell SDRs lately. So directly decoding it could be fast.

Then I found that I could just use machine learning to map SDR back into real values. Bits in grid cell encodes are on and off for a static cycle. It makes intuitive sense some linear model could learn the reverse mapping.

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Turns out it’s mostly the case. Basic machine learning models can learn how to map SDR back into real values. And with the write model, it can resist noise to a certain degree. Basically

  • Generate enough samples from grid cell for a large enough range (ex: -50~50)
    • These are pairs of values. sdr and the associated value
    • Must have enough of them. Otherwise the model will overfit
  • Train a model (ex: Ridge) on the samples
  • Use the model to convert sdr back into real values
  • The sdr can be noisy. It will effect the accuracy but won’t break the model

Please have a look at my code for more detail.

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Good job! That is awesome!

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