Regular spiking in high conductance states: the essential role of inhibition

TL;DR

This study reveals that strong inhibitory inputs in neural networks can stabilize membrane potential fluctuations and influence spike regularity, emphasizing the importance of realistic synaptic models and adaptation mechanisms.

Contribution

It demonstrates that inhibition can decrease membrane potential fluctuations and modulate spike regularity depending on the neuron model's adaptation mechanisms, providing new insights into neural network dynamics.

Findings

Inhibition decreases membrane potential fluctuations in realistic neuron models.

Inhibition increases spike regularity with dynamic thresholds.

Inhibition decreases spike regularity with ionic current adaptation.

Abstract

Strong inhibitory input to neurons, which occurs in balanced states of neural networks, increases synaptic current fluctuations. This has led to the assumption that inhibition contributes to the high spike-firing irregularity observed in vivo. We used single compartment neuronal models with time-correlated (due to synaptic filtering) and state-dependent (due to reversal potentials) input to demonstrate that inhibitory input acts to decrease membrane potential fluctuations, a result that cannot be achieved with simplified neural input models. To clarify the effects on spike-firing regularity, we used models with different spike-firing adaptation mechanisms and observed that the addition of inhibition increased firing regularity in models with dynamic firing thresholds and decreased firing regularity if spike-firing adaptation was implemented through ionic currents or not at all. This novel fluctuation-stabilization mechanism provides a new perspective on the importance of strong inhibitory inputs observed in balanced states of neural networks and highlights the key roles of biologically plausible inputs and specific adaptation mechanisms in neuronal modeling.