Self-sustained activity of low firing rate in balanced networks
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.
Keywords
Cite
@article{arxiv.1809.01020,
title = {Self-sustained activity of low firing rate in balanced networks},
author = {Fernando Borges and Paulo Protachevicz and Rodrigo Pena and Ewandson Lameu and Guilherme Higa and Fernanda Matias and Alexandre Kihara and Chris Antonopoulos and Roberto de Pasquale and Antonio Roque and Kelly Iarosz and Peng Ji and Antonio Batista},
journal= {arXiv preprint arXiv:1809.01020},
year = {2019}
}