Oscillatory network activity arises from interactions between synaptic and intrinsic membrane properties of neurons. In this review, we summarize general mechanisms of synchronous neuronal oscillations. In addition, we focus on recent experimental and computational studies which suggest that activity-dependent changes of ionic environment can affect both the synaptic and intrinsic neuronal properties and influence the network behavior. GABAA receptor (GABAAR)-mediated signaling, that is based on Cl– and HCO3– permeability, is thought to be important for the oscillogenesis and synchronization in cortical networks. A remarkable feature of GABAergic synapses is that prolonged GABAAR activation may lead to switching from a hyperpolarizing to a depolarizing response. This is partly due to a positive shift of the GABAAR reversal potential (EGABA) that is generated by GABA-induced Cl– accumulation in neurons. Recent studies suggest that activity-dependent EGABA changes may have important implications for the mechanisms of gamma oscillations and seizure-like discharges. Thus, a better understanding of the impact of intracellular Cl– dynamics on network behavior may provide insights into the mechanisms of physiological and pathological brain rhythms. Combination of experiments and simulations is a promising approach for elucidating which properties of the time-varying ionic environment can shape the dynamics of a given circuit.
In this paper, the network transmission properties of a feedforward Spiking Neural Network (SNN) affected by synchronous stimuli are investigated with respect to the connection probability and the synaptic strengths. By means of an event-driven method, all simulations are conducted using the Leaky Integrate-and-Fire with Latency (LIFL) model. Typical cases are taken into consideration, in which a network section (module) is able to process the input information, introducing a particular behavior, that we have called path multimodality. Simulation results are discussed. Through this phenomenon, the output layer of the network can generate a number of temporally spaced groups of synchronous spikes. The multimodality effect could be applied for various purposes, for instance in coding or else transmission issues.