Title : Characterizing neuronal activity by describing the membrane potential as a stochastic process
Authors : Martin Pospischil, Zuzanna Piwkowska, Thierry Bal and Alain Destexhe
Year : 2009
Journal : Journal of Physiology - Paris
Volume : 103
Pages : 98-106
Cortical neurons behave similarly to stochastic processes, as a consequence of their irregularity and dense connectivity. Their ?ring pattern is close to a Poisson process, and their membrane potential ðVmÞ is anal- ogous to colored noise. One way to characterize this activity is to identify Vm to a multidimensional sto- chastic process. We review here this approach and how it can be used to extract important statistical signatures of neuronal activity. The ‘‘VmD method” consists of ?tting the Vm distribution obtained intra- cellularly to analytic expressions derived from stochastic processes, and thereby deduce synaptic conduc- tance parameters. However, this method requires at least two levels of Vm, which prevents applications to single-trial measurements. We also discuss methods that can be applied to single Vm traces, such as power spectral analysis and the ‘‘STA method” to calculate spike-triggered average conductances based on a maximum likelihood procedure. A recently proposed method, the ‘‘VmT method”, is based on the fusion of these two concepts. This method is analogous to the VmD method and estimates the mean excitatory and inhibitory conductances and their variances. However, it does so by using a maximum- likelihood estimation, and can thus be applied to single Vm traces. All methods were tested using con- trolled conductance injection in dynamic-clamp experiments.