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. 2014 Apr 29;8:138. doi: 10.3389/fnbeh.2014.00138

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Copyright © 2014 Mandairon, Kermen, Charpentier, Sacquet, Linster and Didier.

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) or licensor 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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Computational modeling of the neural circuits underlying context-driven OB activation. (A) Odor representations in granule cells after training on a randomly chosen odor [mean activity over 200 ms; odor and context (Odor+ctxA), trained context only (No-odor+ctxA), novel, untrained context (No-odor+ctxB)]. (B) Correlation between post-training activation patterns (Odor+ctxA/No-odor+ctxtA and No-odor+ctxA/No-odor+ctxB). (C) Spikes per seconds evoked in granule cells after training in response to the trained odorant and context (Odor+ctxA), the trained context only (No-odor+ctxA), and a novel context (No-odor+ctxB). (D) Spikes per second evoked in pyramidal cells in a naive model exposed to context only (before training; odor naive, No-Odor_naive), to the trained odorant/context (Odor+ctxA), or to the trained context only (No-odor+ctxA). (E) Odor representations in granule cells in response to stimulation with the trained context only with the intact (No-odor+ctxA) or lesioned (No-odor+ctxA-no feedback) feedback. (F) Correlation between Odor+ctxA/No-odor+ctxtA and Odor+ctxA/No-odor+ctxA+no-feedback. (^*p < 0.05).