Information filtering by coincidence detection of synchronous population output: analytical approaches to the coherence function of a two-stage neural system

Information transmission in recurrent networks: Consequences of network noise for synchronous and asynchronous signal encoding

Information about natural time-dependent stimuli encoded by the sensory periphery or communication between cortical networks may span a large frequency range or be localized to a smaller frequency band. Biological systems have been shown to multiplex such disparate broadband and narrow-band signals and then discriminate them in later populations by employing either an integration (low-pass) or coincidence detection (bandpass) encoding strategy. Analytical expressions have been developed for both encoding methods in feedforward populations of uncoupled neurons and confirm that the integration of a population's output low-pass filters the information, whereas synchronous output encodes less information overall and retains signal information in a selected frequency band. The present study extends the theory to recurrent networks and shows that recurrence may sharpen the synchronous bandpass filter. The frequency of the pass band is significantly influenced by the synaptic strengths, especially for inhibition-dominated networks. Synchronous information transfer is also increased when network models take into account heterogeneity that arises from the stochastic distribution of the synaptic weights.

The relationship between snoring sounds and EEG signals on polysomnography

PURPOSE

The aim of this study was to analyze the relationship of snoring sound signals obtained by polysomnography (PSG) in the sleep laboratory with cortical EEG (6 channel) signals to find answers to two important questions that have been covered to a limited extent in the literature: (1) Would the sounds generated by a snoring individual have an effect on the cerebral electrical waves occurring during sleep (specifically deep restorative sleep)? (2) Would the snoring sounds of an individual being examined by PSG have more of an effect on any one of the EEG electrodes?

METHODS

PSG recordings were obtained from volunteers with primary snoring and those with obstructive sleep apnea syndrome (OSAS) on six different EEG channels (F4-M1, C4-M1, and O2-M1, F3-M2, C3-M2, and O1-M2). The relationship of each of these recordings and snoring sound signals was analyzed by using a computer-based electrophysiological signal analysis method. A three-tier approach was used in this relationship: "Feature extraction, Feature selection, and Classification".

RESULTS

Data were obtained from a total of 40 volunteers (32 men, mean age (± SD) 47.5 ± 3.2 years), 20 with primary snoring and 20 with OSAS. The discrete wavelet transform (DWT) feature extraction method was the most successful method, and by utilizing this method for analyzing EEG channels, snoring sound signals were found to affect the C3-M2 channel the most (Duncan test, p < 0.05). Delta wave frequency levels during snoring were decreased compared to both before snoring (p = 0.160) and after snoring (p = 0.04) periods (paired sample test).

DISCUSSION

When snoring sounds and EEG signals were analyzed for frequency, time, and wave conversion with feature extraction methods, the C3-M2 channel was to be found the most affected channel. The sleep physiologist who made the PSG analyses reported that, among the 6 EEG channels analyzed for periods where there was no apnea or hypopnea events but only snoring, C3-M2 was the channel showing changes in delta wave activity.

CONCLUSION

Our study showed that the monotonous and repetitive snoring sounds of the snorer do not wake the individual, but do affect deep restorative sleep (N3). PSG signal analysis revealed that the most significant changes were in the C3-M2 channel (N3 delta wave amplitude increase and frequency decrease during snoring). Thus, clinicians may be able to monitor the characteristic changes occuring in large cortical delta waves in snoring individuals with innovative single-channel EEG devices without microphones.

Recurrence-mediated suprathreshold stochastic resonance

It has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.

Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.