How Structure Determines Correlations in Neuronal Networks

doi:10.1371/journal.pcbi.1002059

How Structure Determines Correlations in Neuronal Networks

Figure 2

Hawkes' theory reproduces rates and correlations in a simulated random network.

Network parameters are . A: Top: spike raster plot showing asynchronous irregular activity (mean coefficient of variation 1.03). Inset: Inter-spike intervals of a typical spike train are exponentially distributed (logarithmic scale). Bottom: Population spike counts in bins of length . Mean standard deviation (thick red line, shaded area), standard deviation from predicted correlations (green dashed line). B: Fluctuating rates of 50 neurons (grey traces), their average (red line) and distribution across time and neurons (blue). A small part reaches below zero (dashed line). C: Simulated time averaged rates scattered vs. predicted rates (blue). Diagonal (red) plotted for direct comparison. Inset: Distribution of predicted (red) and measured (blue) rates. Broad rate distribution with significant deviations from predictions only for small rates. D: Simulated correlations scattered vs. predicted ones. Larger errors due to finite simulation time. Inset: correlation distributions (green: measured, red: predicted). Although a non-vanishing part of fluctuating rates is below zero, most of the time averaged rates and correlations are predicted accurately.

doi: https://doi.org/10.1371/journal.pcbi.1002059.g002