20 years of "noise": Contributions of computational neuroscience to the exploration of the effect of background activity on central neurons

Abstract

The central nervous system is subject to many different forms of noise, which have fascinated researchers since the beginning of electrophysiological recordings. In cerebral cortex, the largest amplitude noise source is the "synaptic noise," which is dominant in intracellular recordings in vivo. The consequences of this background activity are a classic theme of modeling studies. In the last 20 years, this field tremendously progressed as the synaptic noise was measured for the first time using quantitative methods. These measurements have allowed computational models not only to be more realistic and closer to the biological data but also to investigate the consequences of synaptic noise in more quantitative terms, measurable in experiments. As a consequence, the "high-conductance state" conferred by this intense activity in vivo could also be replicated in neurons maintained in vitro using dynamic-clamp techniques. In addition, mathematical approaches of stochastic systems provided new methods to analyze synaptic noise and obtain critical information such as the optimal conductance patterns leading to spike discharges. It is only through such a combination of different disciplines, such as experiments, computational models, and theory, that we will be able to understand how noise participates to neural computations.