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. 2019 Dec 9;9:18612. doi: 10.1038/s41598-019-54859-8

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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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The French-flag detector. (a) Snapshots of the equilibrium (final) network states (V_mem levels) in two detection scenarios. Left: input pattern is the French flag, and the output is depolarized (representing “correct” pattern). Right: input pattern is a highly distorted version of the French flag, and the output is hyperpolarized (representing “incorrect” pattern). Colors represent polarity levels of the cells in units of mV. Notice that the only three intermediate cells that differ in their states between the two cases are those marked with a black star, suggesting that the detector makes minimal use of information to distinguish between patterns. Also notice that the edges are thicker in the right than in the left, suggesting that the voltage-gating dynamics of the edges (representing gap junctions) play a crucial role. (b) The overall performance of the best evolved pattern detector, with data collected from a set of 1000 simulations: 100 parallel sets each with a random sequence of 10 simulations. As expected, the pattern detector classifies patterns similar to the French flag (a total of 500 sample inputs) as “French flag”, and those that are dissimilar (500 inputs) as “not French flag”. This suggests that the pattern detector is robust to noise to a sensible extent. The width of the classification boundary was set implicitly due to the way sample input patterns from the two classes were generated (details in the ‘Methods’ section). (c) The behavior of the best French-flag detector shown for a set of four representative cells (three inputs out of a total of eighteen and one output) for a random sequence of five patterns. Inset highlights the four nodes whose colors correspond with those in the time series. Highlighted in grey squares are the cases where a French-flag-like pattern is input for which the output is depolarized, as expected; for all other cases where a random pattern is shown, the output is hyperpolarized.