A robust balancing mechanism for spiking neural networks (Q6554412)

The aim of the article is to introduce a nonlinear robust mechanism based on short term synaptic depression (STD), which can work with weak external currents too. The network of pulse-coupled phase-oscillators where STD modifies nonlinearly the synaptic inputs is described in the section A of the paper. One considers two coupled populations each of N neurons and introduces the differential equation that models the evolution of the membrane potential \({\nu_j}^{e/i}\) of the jth neuron within the excitatory/inhibitory population. One defines the incoming synaptic currents and the differential equations, modeling the incoming inhibitory and excitatory fields. Assuming depression-dominated synapses, the differential equation modeling the behavior of the synaptic efficacy is defined. Parameter values are provided too. In the B part of the paper, in order to explain how the STD can stabilize a balance state in the absence of strong external currents, a simple self-consistent example is shown. Further one is concerned with the study of the network behavior by performing a set of numerical simulations. For instance, in the C part of the article, one reports the results for a homogeneous network where the firing rate is set to \(\omega\) = 50\,Hz and the phase response curve is \(Z_I\)(\(\phi\)) as it has been actually assumed while building the model. In case of large size neural systems, one investigates if the collective dynamics of the network remains asynchronous. Temporal fluctuation at a single-neuron level is studied. To check robustness of the system, several additional simulations for different parameter values are performed and results are visualized. Conclusions on work are synthesized at the end of the paper.