Self-sustained activity of low firing rate in balanced networks

TL;DR

This paper investigates the phenomenon of self-sustained neural activity at low firing rates in balanced brain networks, combining experimental recordings and numerical simulations to understand its properties and underlying mechanisms.

Contribution

It demonstrates that network parameters like connection probability and size are key to self-sustained activity, bridging experimental data with computational models.

Findings

Self-sustained activity shows high variability and low firing rates.

Network size and connection probability are crucial for sustained activity.

Detailed analysis of lifetime distributions and synaptic dynamics was provided.

Abstract

Self-sustained activity in the brain is observed in the absence of external stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes. In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulate regular spiking (excitatory) and fast-spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of the self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents.