Vitaliy Kolodyazhniy

Roche · Pharma Research and Early Development (pRED)

Background: Circadian and sleep-homeostatic mechanisms regulate timing and quality of wakefulness. To enhance wakefulness, daily consumption of caffeine in the morning and afternoon is highly common. However, the effects of such a regular intake pattern on circadian sleep-wake regulation are unknown. Thus, we investigated if daily daytime caffeine...

To enhance wakefulness, daily consumption of caffeine in the morning and afternoon is highly common. However, it is unknown whether such a regular intake pattern affects timing and quality of wakefulness, as regulated by an interplay of circadian and sleep-homeostatic mechanisms. Thus, we investigated the effects of daily caffeine intake and its wi...

We tested the effect of different lights as a countermeasure against sleep-loss decrements in alertness, melatonin and cortisol profile, skin temperature and wrist motor activity in healthy young and older volunteers under extendend wakefulness. 26 young [mean (SE): 25.0 (0.6) y)] and 12 older participants [(mean (SE): 63.6 (1.3) y)] underwent 40-h...

Sex differences in emotional reactivity have been studied primarily for negative but less so for positive stimuli; likewise, sex differences in the psychophysiological response-patterning during such stimuli are poorly understood. Thus, the present study examined sex differences in response to negative/positive and high/low arousing films (classifi...

Various studies have assessed autonomic and respiratory underpinnings of panic attacks, yet the psychophysiological functioning of panic disorder (PD) patients has rarely been examined under naturalistic conditions at times when acute attacks were not reported. We hypothesized that emotional activation in daily life causes physiologically demonstra...

Rapid eye movement (REM) sleep has been postulated to facilitate emotional processing of negative stimuli. However, empirical evidence is mixed and primarily based on self-report data and picture-viewing studies. This study used a full-length aversive film to elicit intense emotion on one evening, and an emotionally neutral control film on another...

Sleep is regulated in a time-of-day dependent manner and profits working memory. However, the impact of the circadian timing system as well as contributions of specific sleep properties to this beneficial effect remains largely unexplored. Moreover, it is unclear to which extent inter-individual differences in sleep-wake regulation depend on circad...

Sleep loss affects human behavior in a nonuniform manner, depending on the cognitive domain and also the circadian phase. Besides, evidence exists about stable interindividual variations in sleep loss-related performance impairments. Despite this evidence, only a few studies have considered both circadian phase and neurobehavioral domain when inves...

Recently, we developed a novel method for estimating human circadian phase with noninvasive ambulatory measurements combined with subject-independent multiple regression models and a curve-fitting approach. With this, we were able to estimate circadian phase under real-life conditions with low subject burden, i.e., without need of constant routine...

A multilayer spline-based fuzzy neural network (MS-FNN) is proposed. It is based on the concept of multilayer perceptron (MLP) with B-spline receptive field functions (Spline Net). In this paper, B-splines are considered in the framework of fuzzy set theory as membership functions such that the entire network can be represented in form of fuzzy rul...

A multilayer spline-based fuzzy neural network (MS-FNN) is proposed. It is based on the concept of multilayer perceptron (MLP) with B-spline receptive field functions (Spline Net). In this paper, B-splines are considered in the framework of fuzzy set theory as membership functions such that the entire network can be represented in form of fuzzy rul...

The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-ne...

Reliable detection of circadian phase in humans using noninvasive ambulatory measurements in real-life conditions is challenging and still an unsolved problem. The masking effects of everyday behavior and environmental input such as physical activity and light on the measured variables need to be considered critically. Here, we aimed at developing...

This paper presents a digital, transistor level implemented neo-fuzzy neural network. This type of neural network is particularly well suited for real-time applications like those encountered in signal processing and nonlinear system identification. We consider in detail a flexible reconfigurable circuit of a single nonlinear synapse of this networ...

The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering, which is realized with a novel robust evolving recursive fuzzy clustering algorithm that can process incoming observations online (possibly in real-time...

A novel cascaded multiresolution spline-based fuzzy neural network (CMS-FNN) with a very fast constructive training algorithm is introduced. Structurally the CMS-FNN resembles the known cascade-correlation architecture but differs from it in the type of neurons as well as in the training algorithm. The neurons in the CMS-FNN are a generalization of...

A novel cascaded multiresolution spline-based fuzzy neural network (CMS-FNN) with a very fast constructive training algorithm is introduced. Structurally the CMS-FNN resembles the known cascade-correlation architecture but differs from it in the type of neurons as well as in the training algorithm. The neurons in the CMS-FNN are a generalization of...

A spline-based modification of the previously developed Neuro-Fuzzy Kolmogorov's Network (NFKN) is proposed. In order to improve the approximation accuracy, cubic B-splines are substituted for triangular membership functions. The network is trained with a hybrid learning rule combining least squares estimation for the output layer and gradient desc...

A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an exp...

We revisit the problem of representing a high-dimensional data set by a distance-preserving projection onto a two-dimensional plane. This problem is solved by well-known techniques, such as multidimensional scaling. There, the data is projected onto a flat plane and the Euclidean metric is used for distance calculation. In real topographic maps, ho...

This chapter presents new results on modeling 24 hour (circadian) human heart rate data collected with the LifeShirt system using a variety of linear regression and neural network models. Such modeling is important in biopsychology, chronobiology, and chronomedicine where signals collected continuously from human subjects for one or several days ne...

The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering which is realized with a novel robust recursive fuzzy clustering algorithm that can process incoming observations online, and is stable with respect to ou...

A novel Neuro-Fuzzy Kolmogorov's Network (NFKN) is considered. The NFKN is based on the famous Kolmogorov's superposition theorem (KST) and is the development of the previously proposed Fuzzy Kolmogorov's Network (FKN). Modifications of the FKN architecture include multiple outputs as required for classification problems with more than two classes,...

The problem of fuzzy clustering on the basis of the probabilistic and possibilistic approaches under the presence of outliers in data is considered. Robust recursive fuzzy clustering algorithms are proposed, which optimize the objective function suitable for clustering data with heavy-tailed distribution density. Advantages of the proposed algorith...

In this chapter, the problems of identification, modeling, and forecasting of chaotic signals are discussed. These problems are solved with the use of the conventional techniques of computational intelligence as radial basis neural networks and learning neuro-fuzzy architectures, as well as novel hybrid structures based on the Kolmogorov’s superpos...

A recursive learning algorithm based on the rough sets approach to parameter estimation for radial basis function neural networks is proposed. The algorithm is intended for the pattern recognition and classification problems. It can also be applied to neuro control, identification, and emulation.

A new computationally efficient learning algorithm for a hybrid system called further Neuro-Fuzzy Kolmogorov’s Network (NFKN) is proposed. The NFKN is based on and is the development of the previously proposed neural and fuzzy systems using the famous superposition theorem by A.N. Kolmogorov (KST). The network consists of two layers of neo-fuzzy ne...

In the paper, a novel Neuro-Fuzzy Kolmogorov’s Network (NFKN) is considered. The NFKN is based on and is the development of the previously proposed neural and fuzzy systems using the famous Kolmogorov’s superposition theorem (KST). The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer param...

In the paper, a fuzzy filter with finite impulse response (FIR) is considered. The filter is based on the neo-fuzzy neuron architecture. A modification of the neo-fuzzy neuron is proposed, which can be implemented on the basis of conventional FIR-filters and contains fewer membership functions in comparison with the basic architecture. A practical...

A novel fuzzy neural network, called Fuzzy Kolmogorov’s Network (FKN), is proposed. The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and computationally efficient procedures. The validity of theoretical results and the advantages of the F...

A nonlinear self-tuning controller is proposed, which realizes the generalized minimum variance control law. Neo-fuzzy neuron is used as the nonlinear plant model. Zero steady-state error in control of nonlinear systems with a priori unknown structure and parameters is guaranteed. The advantages of the proposed approach consist in lower computation...

A novel fuzzy neural network, called Fuzzy Kolmogorov’s Network (FKN), is considered. The network consists of two layers of neo-fuzzy neurons (NFNs) and is linear in both the hidden and output layer parameters, so it can be trained with very fast and computationally efficient procedures. Two-level structure of the rule base helps the FKN avoid the...

A combined learning algorithm for a self-organizing map (SOM) is proposed. The algorithm accelerates information processing due to the rational choice of the learning rate parameter, and can work when the number of clusters is unknown, as well as when the clusters are overlapping. This is achieved via the introduction of fuzzy inference that deter...

The article addresses the problem of adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both the antecedent and consequent parts of the fuzzy rules is proposed. The algorithm is derived from the Hartley and Marquardt methods. A characteristic feature of the proposed algorithm is t...

In this paper, an architecture of a learning probabilistic neural network is considered. A learning algorithm for the non-conventional activation function parameters is proposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function paramete...

In the paper, a new optimal learning algo- rithm for a neo-fuzzy neuron (NFN) is pro- posed. The algorithm is characteristic in that it provides online tuning of not only the synaptic weights, but also the member- ship functions parameters. The proposed al- gorithm has both the tracking and filtering properties, so the NFN can be effectively used f...

In this paper, an architecture of a resource- allocating learning probabilistic neural net- work is considered. Construction and learn- ing algorithms are proposed. The advan- tages of this network lie in the possibility of classification of data with substantially overlapping clusters. The construction al- gorithm significantly reduces the size of...

In this paper, an architecture of a fuzzy probabilistic neural network is considered. A learning algorithm for the activation function parameters is proposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accu...

The paper addresses the problem of online adaptive learning in a neuro-fuzzy network based on Sugeno-type fuzzy inference. A new learning algorithm for tuning of both antecedent and consequent parts of fuzzy rules is proposed. The algorithm is derived from the well-known Marquardt procedure and uses approximation of the Hessian matrix. A characteri...