Electrical synapses and synchrony: the role of intrinsic currents.
Electrical synapses are ubiquitous in the mammalian CNS. Particularly in the neocortex, electrical synapses have been shown to connect low-threshold spiking (LTS) as well as fast spiking (FS) interneurons. Experiments have highlighted the roles of electrical synapses in the dynamics of neuronal networks. Here we investigate theoretically how intrinsic cell properties affect the synchronization of neurons interacting by electrical synapses. Numerical simulations of a network of conductance-based neurons randomly connected with electrical synapses show that potassium currents promote synchrony, whereas the persistent sodium current impedes it. Furthermore, synchrony varies with the firing rate in qualitatively different ways depending on the intrinsic currents. We also study analytically a network of quadratic integrate-and-fire neurons. We relate the stability of the asynchronous state of this network to the phase-response function (PRF), which characterizes the effect of small perturbations on the firing timing of the neurons. In particular, we show that the greater the skew of the PRF toward the first half of the period, the more stable the asynchronous state. Combining our simulations with our analytical results, we establish general rules to predict the dynamic state of large networks of neurons coupled with electrical synapses. Our work provides a natural explanation for surprising experimental observations that blocking electrical synapses may increase the synchrony of neuronal activity. It also suggests different synchronization properties for LTS and FS cells. Finally, we propose to further test our predictions in experiments using dynamic clamp techniques.
Pfeuty
Benjamin
B
Laboratoire de Neurophysique et Physiologie du Système Moteur, Centre National de la Recherche Scientifique-Unité Mixte de Recherche 8119, Université René Descartes, 75270 Paris Cedex 06, France.
Mato
Germán
G
Golomb
David
D
Hansel
David
D
D003198:Computer Simulation; D008433:Mathematics; D008959:Models, Neurological; D019579:Neocortex / Q000502:physiology; D009431:Neural Conduction / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D011188:Potassium / Q000502:physiology; D012964:Sodium / Q000502:physiology; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors
D012964:Sodium; D011188:Potassium