The A-current and Type I / Type II transition determine collective spiking from common input

TL;DR

This study investigates how the A-type potassium current influences the correlation of neuronal spike trains, revealing that neuron excitability type affects synchronization patterns over different time scales, with implications for neural signal processing.

Contribution

It demonstrates how the A-current modulates spike train correlations in neurons, linking cell physiology to collective firing patterns and downstream signal filtering.

Findings

Type II neurons show stronger short-term correlations.

Type I neurons exhibit more long-term correlated activity.

The A-current influences neural synchronization based on excitability type.

Abstract

The mechanisms and impact of correlated, or synchronous, firing among pairs and groups of neurons is under intense investigation throughout the nervous system. A ubiquitous circuit feature that can give rise to such correlations consists of overlapping, or common, inputs to pairs and populations of cells, leading to common spike train responses. Here, we use computational tools to study how the transfer of common input currents into common spike outputs is modulated by the physiology of the recipient cells. We focus on a key conductance - gA, for the A-type potassium current - which drives neurons between "Type II" excitability (low gA), and "Type I" excitability (high gA). Regardless of gA, cells transform common input fluctuations into a ten- dency to spike nearly simultaneously. However, this process is more pronounced at low gA values, as previously predicted by reduced "phase" models. Thus, for a given level of common input, Type II neurons produce spikes that are relatively more correlated over short time scales. Over long time scales, the trend reverses, with Type II neurons producing relatively less correlated spike trains. This is because these cells' increased tendency for simultaneous spiking is balanced by opposing tendencies at larger time lags. We demonstrate a novel implication for neural signal processing: downstream cells with long time constants are selectively driven by Type I cell populations upstream, and those with short time constants by Type II cell populations. Our results are established via high-throughput numerical simulations, and explained via the cells' filtering properties and nonlinear dynamics.