Spike-frequency adaptation inhibits the pairwise spike correlation - Wang, Jixuan; Deng, Bin; Gao, Tianshi; Wang, Jiang; Tan, Hong (Frontiers in Neuroscience, 2023)
The spike train output correlation with pairwise neurons determines the neural population coding, which depends on the average firing rate of individual neurons. Spike frequency adaptation (SFA), which serves as an essential cellular encoding strategy, modulates the firing rates of individual neurons. However, the mechanism by which the SFA modulates the output correlation of the spike trains remains unclear. We introduce a pairwise neuron model that receives correlated inputs to generate spike trains, and the output correlation is qualified using Pearson's correlation coefficient. The SFA is modeled using adaptation currents to examine its effect on the output correlation. The results showed that the adaptation currents decreased the output correlation by reducing the firing rate of a single neuron. At the onset of a correlated input, a transient process shows a decrease in interspike intervals (ISIs), resulting in a temporary increase in the correlation. When the adaptation current is sufficiently activated, the correlation reached a steady state and the ISIs are maintained at higher values. The enhanced adaptation current achieved by increasing the adaptation conductance further reduces the pairwise correlation. While the time and slide windows influence the correlation, they make no difference in the effect of SFA on decreasing the output correlation. Moreover, we used dynamic thresholds to simulate the SFA, and the decreases in output correlation are consistent with the adaptation currents. The results reveal that the SFA reduces the output correlation with pairwise neurons in the network by reducing the firing rate of individual neuron. Furthermore, a simple phenomenological neuron model with threshold-linear transfer function is utilized to confirm the effect of SFA on decreasing the output correlation. The strength of the signal input and the slope of the linear component of the transfer function, the latter of which can be decreased by SFA, can together modulate the strength of the output correlation. Stronger SFA will decrease the slope and hence decrease the output correlation. This study provides a link between cellular nonlinear mechanisms and network coding strategies.