1. Characterizing neuronal activity by describing the membrane potential as a stochastic process
- Author
-
Zuzanna Piwkowska, Alain Destexhe, Martin Pospischil, Thierry Bal, Institut de Neurobiologie Alfred Fessard (INAF), Centre National de la Recherche Scientifique (CNRS), Unité de neurosciences intégratives et computationnelles (UNIC), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Département d'informatique de l'École normale supérieure (DI-ENS), and École normale supérieure - Paris (ENS-PSL)
- Subjects
Patch-Clamp Techniques ,MESH: Synaptic Potentials ,Models, Neurological ,MESH: Neurons ,Machine learning ,computer.software_genre ,Membrane Potentials ,MESH: Neural Networks (Computer) ,03 medical and health sciences ,0302 clinical medicine ,MESH: Computer Simulation ,MESH: Models, Neurological ,Physiology (medical) ,MESH: Patch-Clamp Techniques ,MESH: Membrane Potentials ,Animals ,MESH: Animals ,Computer Simulation ,030304 developmental biology ,Visual Cortex ,Physics ,Membrane potential ,Neurons ,0303 health sciences ,Computational model ,Stochastic Processes ,Quantitative Biology::Neurons and Cognition ,Stochastic process ,business.industry ,General Neuroscience ,MESH: Visual Cortex ,Conductance ,Synaptic Potentials ,Synaptic noise ,Distribution (mathematics) ,Colors of noise ,MESH: Stochastic Processes ,[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] ,Fokker–Planck equation ,Artificial intelligence ,Neural Networks, Computer ,business ,Biological system ,computer ,030217 neurology & neurosurgery - Abstract
International audience; Cortical neurons behave similarly to stochastic processes, as a consequence of their irregularity and dense connectivity. Their firing pattern is close to a Poisson process, and their membrane potential (V(m)) is analogous to colored noise. One way to characterize this activity is to identify V(m) to a multidimensional stochastic 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 fitting the V(m) distribution obtained intracellularly to analytic expressions derived from stochastic processes, and thereby deduce synaptic conductance parameters. However, this method requires at least two levels of V(m), which prevents applications to single-trial measurements. We also discuss methods that can be applied to single V(m) 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 V(m) traces. All methods were tested using controlled conductance injection in dynamic-clamp experiments.
- Published
- 2009
- Full Text
- View/download PDF