Embedding optimization reveals long-lasting history dependence in neural spiking activity.

ABSTRACT: Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long-potentially redundant-past information can still be read out. Strikingly, we find for model neurons

SUBMITTER: Rudelt L 

PROVIDER: S-EPMC8205186 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature