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. 2019 Jul 9;10:3019. doi: 10.1038/s41467-019-10868-9

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 7 — Distributed model population decoding outperforms two-channel models. a Schematic of three possible spatial coding mechanisms. b–f Performance of the two-channel (hemispheric–blue, opponent–magenta) and distributed (grey) decoders in each stimulus condition from populations of neurons that had significant MI about location. Performance of each decoder was calculated as percentage of correctly classified individual trials. The dotted line indicates chance (1/6). The black line and coloured bands indicate the mean ± s.d. of the performance for 250 different random subpopulations of each size drawn from the full sample of cells. g Spatial receptive fields for all units used in the population analysis for BBN, ordered by best position. h Decoder performance for models with increasing channel numbers with units grouped according to g. i Decoder performance when units are randomly ordered and assigned to channels. j Comparison of decoder performance and shuffled performance for a fixed population of 120 cells divided into increasing numbers of channels. k Effect of the number of units per channel using the data from modelled unit responses for ordered and shuffled units (see the Methods)