Timescales of spike-train correlation for neural oscillators with common drive

TL;DR

This paper investigates how different neural oscillator types transfer input correlations into output spike correlations across various timescales, revealing a switch in transfer efficiency between Type I and Type II models.

Contribution

It introduces a linear response framework to compare correlation transfer in Type I and Type II neural oscillators over multiple timescales.

Findings

Type I oscillators transfer correlations more efficiently over long timescales.

Type II oscillators are more efficient at short timescales.

The switch in correlation transfer efficiency depends on input current statistics.

Abstract

We examine the effect of the phase-resetting curve (PRC) on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy, super-threshold stimulation. We use linear response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem, and contrast the results for Type I vs. Type II models and across the different timescales over which spike correlations can be assessed. We find that, on long timescales, Type I oscillators transfer correlations much more efficiently than Type II oscillators. On short timescales this trend reverses, with the relative efficiency switching at a timescale that depends on the mean and standard deviation of input currents. This switch occurs over timescales that could be exploited by downstream circuits.