Towards predicting persistent activity of neurons by statistical and fractal dimension-based features

Persistent activity is the prolongation of neuronal firing that outlasts the presentation of a stimulus and has been recorded during the execution of working memory tasks in several cortical regions. The emergence of persistent activity is stimulus-specific: not all inputs lead to persistent firing, only `preferred' ones. However, the features of a stimulus or the stimulus-induced response that determine whether it will ignite persistent activity remain unknown. In this paper, we propose various statistical and fractal dimension-based features derived from the activity of a detailed biophysical Prefrontal Cortex microcircuit model, for the efficient classification of the upcoming Persistent or Non-Persistent-activity state. Moreover, by introducing a novel majority voting classification framework we manage to achieve classification rates up to 92.5%, suggesting that selected features carry important predictive information that may be read out by the brain in order to identify `preferred' vs. `no-preferred' stimuli.

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