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Front Comput Neurosci. 2015 Jul 29;9:96. doi: 10.3389/fncom.2015.00096. eCollection 2015.

Synaptic dynamics and neuronal network connectivity are reflected in the distribution of times in Up states.

Author information

1
IBENS, Ecole Normale Supérieure, Applied Mathematics and Computational Biology Paris, France.
2
Institute of Neuroscience, Technische Universität München Munchen, Germany.
3
Institut de Neurobiologie de la Méditerranée-INSERM U901 Marseille, France.

Abstract

The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.

KEYWORDS:

Up states; first passage times; inverse problem; mean-field model; modeling; neuronal networks; non-poissonnian distribution; synaptic depression

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