Qualitative and quantitative aspects of synchronization in coupled CA1 pyramidal neurons

Robust Master‐Slave Synchronization of Neuronal Systems

“…Other model parameters for the base numerical simulation are [19,25] ,1Na = ,1Na = 115.0 mV, ,1l = ,1l = 10.599 mV, ,1k = 7.2 mV, ,1k = −12.0 mV (representing the equilibrium potentials of the sodium, leak, and potassium, resp. ), Na, = Na, = 120.0 ms/cm As shown in Figure 2, HH neurons exhibit different simple periodic dynamical behavior before the activation of the synchronizer.…”

“…Phase coupling consists of modeling each member of the population as a phase oscillator and coupling them through the sine of their phase differences [21]. For instance, Wang et al [19] applied phase differences to study different states of synchrony in two electrically coupled neurons.…”

“…Diffusive coupling via gap junctions is considered as the natural form of coupling in many neuronal processes [19][20][21]. Gap junctions can be written as a particular form of diffusive coupling.…”

“…Synchronization approaches in neuronal systems are aimed at exploring the communication between neurons with the computing of coupling functions that resemble observed experimental electrical cell activity [6][7][8][9][10]. From the general synchronization point of view, synchronization approaches can be classified into two general groups [11,12]: (i) natural coupling (self-synchronization) [13][14][15][16][17][18][19][20][21] and (ii) artificial coupling using state observers or feedback control approaches [22][23][24][25][26][27][28][29][30][31][32][33][34].…”

“…Classical approaches to the problem of neuronal synchronization include diffusive and phase couplings [12][13][14][15][16][17][18][19][20][21]. Diffusive coupling via gap junctions is considered as the natural form of coupling in many neuronal processes [19][20][21].…”