Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our knowledge, these results are the first demonstration that the brain contains, and may exploit, powerful error-correcting codes for analog variables.
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We are grateful to Y. Burak, S. Seung, A. Kepecs, P. Latham, A. Pouget and P. Dayan for thought-provoking questions and comments, S.H. Lee for helpful conversations, T. Lam for advice on a volume computation, D. Johnston, A. Huk, N. Priebe, A. Tan and members of our laboratory for comments on the manuscript, and A. Preston for useful pointers to the literature. I.F. is a Sloan Foundation Fellow, a Searle Scholar and a McKnight Scholar, and receives funding from the Office of Naval Research through the Multidisciplinary University Research Initiative.
The authors declare no competing financial interests.
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Sreenivasan, S., Fiete, I. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nat Neurosci 14, 1330–1337 (2011). https://doi.org/10.1038/nn.2901
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