2011/11/02 by Georgi S. Medvedev, Svitlana Zhuravytska · 15 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · Physics and Astronomy · #Artificial neural network #Bursting #Complex network #Complex system #Coupling (piping) #Electrical network #Molecular Communication and Nanonetworks #Neural dynamics and brain function #Noise (video) #Noise reduction #Topology (electrical circuits) #nlin.AO #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.1007/s00422-012-0481-y
published in Biological Cybernetics 106(2), 67-88 (Springer Science+Business Media)
arxiv created 2011/11/02 · openalex publication_date 2012/02/01 · arxiv updated 2012/06/05 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Gap-junctional coupling is an important way of communication between neurons and other excitable cells. Strong electrical coupling synchronizes activity across cell ensembles. Surprisingly, in the presence of noise synchronous oscillations generated by an electrically coupled network may differ qualitatively from the oscillations produced by uncoupled individual cells forming the network. A prominent example of such behavior is the synchronized bursting in islets of Langerhans formed by pancreatic β-cells, which in isolation are known to exhibit irregular spiking. At the heart of this intriguing phenomenon lies denoising, a remarkable ability of electrical coupling to diminish the effects of noise acting on individual cells. In this paper, we derive quantitative estimates characterizing denoising in electrically coupled networks of conductance-based models of square wave bursting cells. Our analysis reveals the interplay of the intrinsic properties of the individual cells and network topology and their respective contributions to this important effect. In particular, we show that networks on graphs with large algebraic connectivity or small total effective resistance are better equipped for implementing denoising. As a by-product of the analysis of denoising, we analytically estimate the rate with which trajectories converge to the synchronization subspace and the stability of the latter to random perturbations. These estimates reveal the role of the network topology in synchronization. The analysis is complemented by numerical simulations of electrically coupled conductance-based networks. Taken together, these results explain the mechanisms underlying synchronization and denoising in an important class of biological models.