Martijn Arts

Interdependent Conductances Drive Infraslow Intrinsic Rhythmogenesis in a Subset of Accessory Olfactory Bulb Projection Neurons

The accessory olfactory system controls social and sexual behavior. However, key aspects of sensory signaling along the accessory olfactory pathway remain largely unknown. Here, we investigate patterns of spontaneous neuronal activity in mouse accessory olfactory bulb mitral cells, the direct neural link between vomeronasal sensory input and limbic output. Both in vitro and in vivo, we identify a subpopulation of mitral cells that exhibit slow stereotypical rhythmic discharge. In intrinsically rhythmogenic neurons, these periodic activity patterns are maintained in absence of fast synaptic drive. The physiological mechanism underlying mitral cell autorhythmicity involves cyclic activation of three interdependent ionic conductances: subthreshold persistent Na+ current, R-type Ca2+ current, and Ca2+-activated big conductance K+ current. Together, the interplay of these distinct conductances triggers infraslow intrinsic oscillations with remarkable periodicity, a default output state likely to affect sensory processing in limbic circuits. SIGNIFICANCE STATEMENT We show for the first time that some rodent accessory olfactory bulb mitral cells—the direct link between vomeronasal sensory input and limbic output—are intrinsically rhythmogenic. Driven by ≥3 distinct interdependent ionic conductances, infraslow intrinsic oscillations show remarkable periodicity both in vitro and in vivo. As a novel default state, infraslow autorhythmicity is likely to affect limbic processing of pheromonal information.

Analytical test statistic distributions of the MMME eigenvalue-based detector for spectrum sensing

We present an analytical derivation of the probability density functions (PDFs) of the maximum-minus-minimum eigenvalue (MMME) detector for the special case of two cooperating secondary users (SUs) in a spectrum sensing scenario. For this we employ a simple additive white Gaussian noise (AWGN) model, where in general K cooperating SUs are monitoring the wireless spectrum to determine the presence of a single primary user, which transmits phase shift keying (PSK) modulated signals. The sample covariance matrix is aWishart matrix under both the noise only and the signal plus noise hypothesis under this model. For K = 2, we derive the exact PDFs for the MMME detector under both hypotheses for a finite number of samples N taken. Then, we compare the performance of the MMME detector and the maximum-minimum eigenvalue (MME) detector aided by exact PDFs available in the literature for this model. Finally, we analyze the noise power uncertainty tolerance margin of the MMME detector under which it shows superior performance to the MME detector.

Exact quickest spectrum sensing algorithms for eigenvalue-based change detection

We study a collaborative quickest detection scheme that uses a function of the eigenvalues of the sample covariance matrix for a spectrum sensing system with a fusion center. A simple model consisting of one potentially present primary user (PU), which utilizes phase shift keying (PSK), and the standard additive white Gaussian noise (AWGN) assumption is considered. Here, for both detection hypothesis, the sample covariance matrix follows a Wishart distribution. For K = 2 collaborating secondary users (SUs), the probability distribution function (PDF) of the maximum-minimum eigenvalue (MME) test statistic can be derived analytically under both hypotheses, allowing us to develop exact quickest detection algorithms for known and unknown SNR. We analyze the two types of change detection problems in spectrum sensing, i.e., the channel becoming free when it was occupied before and vice versa. Performance evaluation is done by evaluating bounds and by comparing the presented quickest detection algorithms with the traditional block detection scheme.

An Information Theoretic Approach to Stimulus Processing in the Olfactory System

Biological communication and information systems have evolved over millions of years. Although they have been optimized under different design criteria than recent man-made technical communication systems, both are subject to the same information theoretic principles. It is the purpose of this proposal to design manageable channel models which describe information flow and signal processing by cellular and neural entities. In biology, channels are formed by transmitting intertwined chemical and electrical stimuli. A typical, however, still tractable example is the olfactory system of mammals. Mice will be used as a model to explore the basic principles of information exchange between sensory neurons and the brain by information theoretic means. Massive parallelism, optimal quantization, and information fusion will be important challenges to cope with. The final goal of this proposal is twofold. First, biologists will be provided with analytical models to simulate certain aspects of neural processes on a purely numerical basis. Second, the functionality of biological transmission channels will be explored, the basic principles will be isolated and useful features will be carried over to technical communication systems.

Performance limits of cooperative eigenvalue-based spectrum sensing under noise calibration uncertainty

A collaborative spectrum sensing system with a fusion center, which utilizes the eigenvalues of the sample covariance matrix for detection, is investigated. On the example of two widely known detectors, we show that imperfect calibration with respect to the noise powers of the receivers leads to a so-called SNR wall in eigenvalue-based detectors. The SNR wall manifests itself as an SNR threshold below which detection becomes impossible even if the number of samples tends to infinity. We quantify the performance limits of the detectors in question by deriving lower bounds on the SNR wall and verify them by numerical evaluation. The results show that a very large number of cooperating receivers is needed to enable detection at very low SNRs, which are customary in spectrum sensing.

A full digital magnetic induction measurement device for non-contact vital parameter monitoring (MONTOS)

Magnetic induction measurements enable contactless monitoring of breathing and heart activity. Since this technique is in the scope of many research groups, there are several research devices available. Most of these devices are suitable for tomography approaches, e.g. edema detection or for monitoring technical processes, such as fluid in tubes or metal blocks. However, these devices are less useable for vital parameter monitoring. In this article, we present an new modular magnetic induction measurement system called MONTOS (Monitoring System) for this scenario. Since the implementation is fully digital, each module can easily be applied to several measurement conditions in vital parameter monitoring, i.e. Multi-Frequency measurement modes, Single-Excitation and Multiple-Measurements or Multiple-Excitation and Single-Measurement. Data output is realized via local area networks (LAN), thereby streaming the data to a monitoring computer. Finally, it will be demonstrated that impedance changes due to breathing of a human adult can be detected.