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. 2023 Jun 5;17:1175629. doi: 10.3389/fnins.2023.1175629

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Copyright © 2023 Marsat, Daly and Drew.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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The presence of noise correlations decreases the information content and the discriminability estimate proportionally. Noise-induced correlation across responses of different neurons in a population can decrease the total information content of the population compared to similar responses where the noise is not correlated. Our population of model neurons showed this effect since the population with noise correlations had up to 60% less information that the same responses for which noise correlations were shuffled out. This change in information content is mirrored by a decrease in discrimination accuracy. The figure shows the results for the 3 versions of the analysis used in previous figures: (A) constant weights across neurons and time points; (B) weights fixed across time and neural responses averaged across neurons before comparison; (C) weight optimized independently across neurons and time points and all the dimensions kept separate. The number of neurons included in the analysis was varied as in previous figures (the different points along each curve here) and the noise in the LIF model was set to 3 different levels (varying shade).