English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The repeated presentation of an identical visual stimulus in the receptivefield of a neuron may evoke different spiking patterns at each trial.Probabilistic methods are essential to understand the functional role of thisvariance within the neural activity. In that case, a Poisson process is themost common model of trial-to-trial variability. For a Poisson process, thevariance of the spike count is constrained to be equal to the mean,irrespective of the duration of measurements. Numerous studies have shown thatthis relationship does not generally hold. Specifically, a majority ofelectrophysiological recordings show an " over-dispersion " effect: Responsesthat exhibit more inter-trial variability than expected from a Poisson processalone. A model that is particularly well suited to quantify over-dispersion isthe Negative-Binomial distribution model. This model is well-studied and widelyused but has only recently been applied to neuroscience. In this paper, weaddress three main issues. First, we describe how the Negative-Binomialdistribution provides a model apt to account for overdispersed spike counts.Second, we quantify the significance of this model for any neurophysiologicaldata by proposing a statistical test, which quantifies the odds thatover-dispersion could be due to the limited number of repetitions (trials). Weapply this test to three neurophysiological tests along the visual pathway.Finally, we compare the performance of this model to the Poisson model on apopulation decoding task. We show that the decoding accuracy is improved whenaccounting for over-dispersion, especially under the hypothesis of tunedover-dispersion.

Testing the Odds of Inherent versus Observed Over-dispersion in Neural Spike Counts Odds of Inherent versus Observed Over-dispersion