Measuring spike train synchrony

Estimating the degree of synchrony or reliability between two or more spike trains is a frequent task in both experimental and computational neuroscience. In recent years, many different methods have been proposed that typically compare the timing of spikes on a certain time scale to be fixed beforehand. Here, we propose the ISI-distance, a simple complementary approach that extracts information from the interspike intervals by evaluating the ratio of the instantaneous frequencies. The method is parameter free, time scale independent and easy to visualize as illustrated by an application to real neuronal spike trains obtained in vitro from rat slices. In a comparison with existing approaches on spike trains extracted from a simulated Hindemarsh-Rose network, the ISI-distance performs as well as the best time-scale-optimized measure based on spike timing.Comment: 11 pages, 13 figures; v2: minor modifications; v3: minor modifications, added link to webpage that includes the Matlab Source Code for the method (http://inls.ucsd.edu/~kreuz/Source-Code/Spike-Sync.html

A Graph-based Framework for Transmission of Correlated Sources over Broadcast Channels

In this paper we consider the communication problem that involves transmission of correlated sources over broadcast channels. We consider a graph-based framework for this information transmission problem. The system involves a source coding module and a channel coding module. In the source coding module, the sources are efficiently mapped into a nearly semi-regular bipartite graph, and in the channel coding module, the edges of this graph are reliably transmitted over a broadcast channel. We consider nearly semi-regular bipartite graphs as discrete interface between source coding and channel coding in this multiterminal setting. We provide an information-theoretic characterization of (1) the rate of exponential growth (as a function of the number of channel uses) of the size of the bipartite graphs whose edges can be reliably transmitted over a broadcast channel and (2) the rate of exponential growth (as a function of the number of source samples) of the size of the bipartite graphs which can reliably represent a pair of correlated sources to be transmitted over a broadcast channel.Comment: 36 pages, 9 figure

A Multimodal Approach for the Assessment of Alexithymia: An Evaluation of Physiological, Behavioral, and Self-Reported Reactivity to a Traumatic Event-Relevant Video

Evidence suggests alexithymia is often relatively elevated among people suffering from posttraumatic stress symptoms (PTSS). Despite a growing body of research supporting this relation between alexithymia and PTSS, it is unclear whether alexithymia is a unique predictor of emotional reactivity relative to posttraumatic stress symptoms. Furthermore, existing literature is largely limited to retrospective, self-reported symptoms. Therefore, the current study employed a multimodal assessment strategy for measuring emotional reactivity in the context of posttraumatic stress. More specifically, self-report, behavioral, and physiological measures were used to measure emotional responding to a traumatic event-related stimulus among motor vehicle accident victims. It was hypothesized that behavioral and self-reported responding would evidence a negative relation to level of alexithymia, while physiological responding was not expected to relate to levels of alexithymia. Results replicated previous research demonstrating a strong correlation between self-reported PTSS and alexithymia. Also as expected, alexithymia did not predict physiological responding to the stimulus. However, alexithymia was not found to uniquely predict self-reported or behavioral responding above and beyond the influence of PTSS. These findings do not conclusively support alexithymia as a unique predictor of emotional responding relative to PTSS

EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

Short Packet Structure for Ultra-Reliable Machine-type Communication: Tradeoff between Detection and Decoding

Machine-type communication requires rethinking of the structure of short packets due to the coding limitations and the significant role of the control information. In ultra-reliable low-latency communication (URLLC), it is crucial to optimally use the limited degrees of freedom (DoFs) to send data and control information. We consider a URLLC model for short packet transmission with acknowledgement (ACK). We compare the detection/decoding performance of two short packet structures: (1) time-multiplexed detection sequence and data; and (2) structure in which both packet detection and data decoding use all DoFs. Specifically, as an instance of the second structure we use superimposed sequences for detection and data. We derive the probabilities of false alarm and misdetection for an AWGN channel and numerically minimize the packet error probability (PER), showing that for delay-constrained data and ACK exchange, there is a tradeoff between the resources spent for detection and decoding. We show that the optimal PER for the superimposed structure is achieved for higher detection overhead. For this reason, the PER is also higher than in the preamble case. However, the superimposed structure is advantageous due to its flexibility to achieve optimal operation without the need to use multiple codebooks.Comment: Accepted at ICASSP 2018, special session on "Signal Processing for Machine-Type Communications

Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1