Mehmet K. Muezzinoglu

Dynamical principles of emotion-cognition interaction: mathematical images of mental disorders.

The key contribution of this work is to introduce a mathematical framework to understand self-organized dynamics in the brain that can explain certain aspects of itinerant behavior. Specifically, we introduce a model based upon the coupling of generalized Lotka-Volterra systems. This coupling is based upon competition for common resources. The system can be regarded as a normal or canonical form for any distributed system that shows self-organized dynamics that entail winnerless competition. Crucially, we will show that some of the fundamental instabilities that arise in these coupled systems are remarkably similar to endogenous activity seen in the brain (using EEG and fMRI). Furthermore, by changing a small subset of the systems parameters we can produce bifurcations and metastable sequential dynamics changing, which bear a remarkable similarity to pathological brain states seen in psychiatry. In what follows, we will consider the coupling of two macroscopic modes of brain activity, which, in a purely descriptive fashion, we will label as cognitive and emotional modes. Our aim is to examine the dynamical structures that emerge when coupling these two modes and relate them tentatively to brain activity in normal and non-normal states.

Chemosensor-driven artificial antennal lobe transient dynamics enable fast recognition and working memory

The speed and accuracy of odor recognition in insects can hardly be resolved by the raw descriptors provided by olfactory receptors alone due to their slow time constant and high variability. The animal overcomes these barriers by means of the antennal lobe (AL) dynamics, which consolidates the classificatory information in receptor signal with a spatiotemporal code that is enriched in odor sensitivity, particularly in its transient. Inspired by this fact, we propose an easily implementable AL-like network and show that it significantly expedites and enhances the identification of odors from slow and noisy artificial polymer sensor responses. The device owes its efficiency to two intrinsic mechanisms: inhibition (which triggers a competition) and integration (due to the dynamical nature of the network). The former functions as a sharpening filter extracting the features of receptor signal that favor odor separation, whereas the latter implements a working memory by accumulating the extracted features in trajectories. This cooperation boosts the odor specificity during the receptor transient, which is essential for fast odor recognition.

TRANSIENTS VERSUS ATTRACTORS IN COMPLEX NETWORKS

Understanding and predicting the behavior of complex multiagent systems like brain or ecological food net requires new approaches and paradigms. Traditional analyses based on just asymptotic results of behavior as time goes to infinity, or on straightforward mathematical images that can accommodate only fixed points or limit cycles do not tell much about these systems. To obtain sensible dynamical models of natural phenomena, such as the reproducible order observed in ecological, cognitive or behavioral experiments, one cannot afford to neglect the transient dynamics of the underlying complex network. In disclosing such dynamical mechanisms, the focus of interest must be on reproducible or, even, structurally stable transients. In this tutorial, we formulate the Winnerless Competition (WLC) principle that induces robust transient dynamics in open complex networks. The main point of WLC principle is the transformation of the acquired information into ensemble (spatio)-temporal output via intrinsic transient dynamics of the network. Such encoding provides a reproducible transient response, whose geometrical image in phase space is a stable heteroclinic sequence. We compile a diverse list of natural phenomena which can be rigorously modeled by the WLC. Together with the experimental and numerical results of the networks with different levels of complexity, we evaluate the robustness and reproducibility of the WLC dynamics and discuss the advantages of future possible application of the discussed approach.

A unified framework for Volatile Organic Compound classification and regression

Motivated by the insect olfactory system, which resolves both the identity and the quantity of a nectar in parallel based on the same sensory cue, we address the problem of Volatile Organic Compound (VOC) classification and regression in a unified setting. We derive a maximum margin formulation for minimizing the empirical regression error and the classification error jointly, and then call the sequential minimal optimization procedure for solution. The solution yields a pool of support vectors that achieves both tasks almost equally accurately as individual performances of a support vector machine classifier and a support vector regressor designed independently. We investigate empirically the advantages and inconveniences of handling these two problems under a single formulation for odor identification and quantification. We demonstrate the method on an extensive dataset acquired by an array metal-oxide sensors for five VOC identities and a wide range of concentrations.

Localization of remote odor sources by metal-oxide gas sensors in turbulent plumes

Numerous applications reported in the sensors literature have established metal-oxide gas sensors as effective devices for detecting and quantifying a broad range of chemical events. A critical question that could substantially expand their usage is the following: can they characterize the spatio-temporal stimulus characteristics for predicting the source location? We show that commercially-available metal-oxide sensors have a high enough temporal resolution for such a characterization. By selecting proper features of the sensor response and by maintaining a representative response database recorded from known locations in similar plumes, simple short-time measurements can accurately predict the displacement from the source to the sensing location. A key advantage of such a prediction scheme is its achieving the localization remotely and without a navigation within the plume.

Kullback-Leibler distance optimization for artificial chemo-sensors

A gas sensor optimization method for odor discrimination is introduced in this paper. The method deals with a performance index widely used in the information theory, namely the Kullback-Leibler distance (KL-distance), which gives a quantitative measure of mutual difference between two probability distributions. We argue that optimizing this index over the controllable operating parameter namely β (i.e., the operating temperature) of a single sensor will allow maximizing the spread of the odor-class prototypes (i.e., the class centers) in the feature space so that a better discrimination of odorants will be possible. We demonstrate on a sample dataset that finely tuning the operating temperature of a metal oxide sensor based on the suggested criterion not only yields a substantial improvement in classification performance but also warns about the existence of temperatures that cause a total confusion in the odor discrimination.

Metastability and Transients in Brain Dynamics: Problems and Rigorous Results

Experimental neuroscience is often based on the implicit premise that the neural mechanisms underlying perception, emotion and cognition are well approximated by steady-state measurements of neuron activity or snapshot of images. We will unfold a new paradigm in the study of brain mental dynamics departing from the stable transient activity neural networks, as supported by experiments. Transients have two main features: (1) they are resistant to noise, and reliable even in the face of small variations in initial condition, (2) the transients are input-specific, and thus convey information about what caused them in the first place. This new dynamical view manifests a rigorous explanation of how perception, cognition, emotion, and other mental processes evolve as a sequence of metastable states in the brain and suggests the new approaches to the diagnostics of mental diseases. The mathematical image of robust and sensitive transients is a stable heteroclinic channel that is possibly the only dynamical object that satisfies all required conditions. We discuss the ideas that lead to the creation of a quantitative theory of mental human activity. For the convenience of the reader we put all mathematical details into Appendices.

Using a cellular neural network based olfactory bulb model for choosing the best sensor temperature for an odor classification problem

In this paper, a part of mamal olfaction system, olfactory bulb, is modelled by a Cellular Ceural Network and the performance of the model in an odor classification problem is evaluated for different sensör temperatures in order to figure out in which sensör temperature the most distinguishable data is recorded. The relevant probem in odor classification task is the slowy changing time response of the odor sensors and the model presented in this work is a structure that can be used to speed up odor processing.