RESUMO

Internal stochastic resonance (internal SR) is a phenomenon of non-linear systems in which the addition of a non-zero level of noise produces an enhancement in the coherence between two or more signals. In a previous study, we found that the simultaneous administration of multisensory visual and auditory noise augments global coherence in electroencephalographic (EEG) signals via this phenomenon. Here, we examined whether such global coherence can also be augmented with at least one noisy acoustic source. We performed experiments on healthy subjects and applied the following binaural and monaural noise-stimulation protocols. First, we administered to the left ear Gaussian noise of fixed intensity, while we delivered to the right ear a second Gaussian noise of variable intensity levels (binaural protocol). Second, we applied the Gaussian noise of the same variable intensity levels but only to one ear (monaural protocol). We performed a permutation test analysis, finding that during both noise protocols there was a significant enhancement in the global coherence in EEG signals via the occurrence of internal SR within central pathways of the auditory system.

Assuntos

Eletroencefalografia , Ruído , Estimulação Acústica , Humanos

RESUMO

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdos-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.