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Search results for: NEURON MODEL
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Wild oscillations in a nonlinear neuron model with resets: (II) Mixed-mode oscillations
PublicationThis work continues the analysis of complex dynamics in a class of bidimensional nonlinear hybrid dynamical systems with resets modeling neuronal voltage dynamics with adaptation and spike emission. We show that these models can generically display a form of mixed-mode oscillations (MMOs), which are trajectories featuring an alternation of small oscillations with spikes or bursts (multiple consecutive spikes). The mechanism by...
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Spike patterns and chaos in a map-based neuron model
PublicationThe work studies the well-known map-based model of neuronal dynamics introduced in 2007 by Courbage, Nekorkin and Vdovin, important due to various medical applications. We also review and extend some of the existing results concerning β-transformations and (expanding) Lorenz mappings. Then we apply them for deducing important properties of spike-trains generated by the CNV model and explain their implications for neuron behaviour....
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Periodic and chaotic dynamics in a map‐based neuron model
PublicationMap-based neuron models are an important tool in modeling neural dynamics and sometimes can be considered as an alternative to usually computationally costlier models based on continuous or hybrid dynamical systems. However, due to their discrete nature, rigorous mathematical analysis might be challenging. We study a discrete model of neuronal dynamics introduced by Chialvo in 1995. In particular, we show that its reduced one-dimensional...
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Topological-numerical analysis of a two-dimensional discrete neuron model
PublicationWe conduct computer-assisted analysis of a two-dimensional model of a neuron introduced by Chialvo in 1995 [Chaos, Solitons Fractals 5, 461–479]. We apply the method of rigorous analysis of global dynamics based on a set-oriented topological approach, introduced by Arai et al. in 2009 [SIAM J. Appl. Dyn. Syst. 8, 757–789] and improved and expanded afterward. Additionally, we introduce a new algorithm to analyze the return times...
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AMO model for neuro-inclusive remote workplace
PublicationPurpose The aim of this article is to extend current debates on organizational equality, diversity and inclusion to a consideration of neurodivergence in the remote workplace context. Design/methodology/approach Drawing on the ability, motivation, and opportunity (AMO) model and an emerging strength-based approach to neurodiversity, this conceptual paper integrates research on neurodiversity at work and remote working to provide...
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Analysis of dynamics of a map-based neuron model via Lorenz maps
PublicationModeling nerve cells can facilitate formulating hypotheses about their real behavior and improve understanding of their functioning. In this paper, we study a discrete neuron model introduced by Courbage et al. [Chaos 17, 043109 (2007)], where the originally piecewise linear function defining voltage dynamics is replaced by a cubic polynomial, with an additional parameter responsible for varying the slope. Showing that on a large...
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Neural reliability model of diesel engines
PublicationW artykule przedstawiono wyniki weryfikacji hipotezy zakładającej celowość zastosowania modelu niezawodnościowego silnika tłokowego z zapłonem samoczynnym w postaci sztucznej sieci neuronowej. Weryfikację przeprowadzono w oparciu o wyniki badań eksploatacyjnych.
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Differential models versus neural models in optimisation
PublicationW pracy porównano zastosowanie modeli różniczkowych i modeli neuronowych dla celów optymalizacji.
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Concept of Neural Model of the Sea Bottom Surface
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Wild oscillations in a nonlinear neuron model with resets: (I) Bursting, spike-adding and chaos
PublicationIn a series of two papers, we investigate the mechanisms by which complex oscillations are generated in a class of nonlinear dynamical systems with resets modeling the voltage and adaptation of neurons. This first paper presents mathematical analysis showing that the system can support bursts of any period as a function of model parameters, and that these are organized in a period-incrementing structure. In continuous dynamical...
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Type III Responses to Transient Inputs in Hybrid Nonlinear Neuron Models
PublicationExperimental characterization of neuronal dynamics involves recording both of spontaneous activity patterns and of responses to transient and sustained inputs. While much theoretical attention has been devoted to the spontaneous activity of neurons, less is known about the dynamic mechanisms shaping their responses to transient inputs, although these bear significant physiological relevance. Here, we study responses to transient...
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Neural Network Subgraphs Correlation with Trained Model Accuracy
PublicationNeural Architecture Search (NAS) is a computationally demanding process of finding optimal neural network architecture for a given task. Conceptually, NAS comprises applying a search strategy on a predefined search space accompanied by a performance evaluation method. The design of search space alone is expected to substantially impact NAS efficiency. We consider neural networks as graphs and find a correlation between the presence...
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An adaptive neuro-fuzzy model of a re-heat two-stage adsorption chiller
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Optimization of multiple model neural tracking filter for marine targets
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Verification of multiple model neural tracking filter with ship's radar
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A Bayesian regularization-backpropagation neural network model for peeling computations
PublicationA Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network...
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Global Surrogate Modeling by Neural Network-Based Model Uncertainty
PublicationThis work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the model uncertainty. Specifically, the algorithm proceeds in cycles and adaptively enhances the neural network-based surrogate model by selecting the next sampling points guided by an auxiliary neural network approximation of the spatial error. The proposed algorithm is tested numerically...
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A MODEL FOR FORECASTING PM10 LEVELS WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
PublicationThis work presents a method of forecasting the level of PM10 with the use of artificial neural networks. Current level of particulate matter and meteorological data was taken into account in the construction of the model (checked the correlation of each variable and the future level of PM10), and unidirectional networks were used to implement it due to their ease of learning. Then, the configuration of the network (built on the...
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OBTAINING FLUID FLOW PATTERN FOR TURBINE STAGE WITH NEURAL MODEL.
PublicationIn the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stage for fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy...
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Finite Element model updating on experimental modal parameters
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Comparison of the Ability of Neural Network Model and Humans to Detect a Cloned Voice
PublicationThe vulnerability of the speaker identity verification system to attacks using voice cloning was examined. The research project assumed creating a model for verifying the speaker’s identity based on voice biometrics and then testing its resistance to potential attacks using voice cloning. The Deep Speaker Neural Speaker Embedding System was trained, and the Real-Time Voice Cloning system was employed based on the SV2TTS, Tacotron,...
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Creating neural models using an adaptive algorithm for optimal size of neural network and training set.
PublicationZaprezentowano adaptacyjny algorytm generujący modele neuronowe liniowych układów mikrofalowych, zdolny do oszacowania optymalnego rozmiaru zbiory uczącego i sieci neuronowej. Stworzono kilka modeli nieciągłości falowodowych i mokropaskowych, a następnie zweryfikowano ich poprawność porównując wyniki analiz metodą dopasowania rodzajów i metodą momentów filtrów pasmowo-przepustowych.
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EPILEPTIC BEHAVIOR WITH A DISTINGUISHED PREICTAL PERIOD IN A LARGE-SCALE NEURAL NETWORK MODEL
PublicationWe present a neural network model capable of reproducing focal epileptic behavior. An important property of our model is the distinguished preictal state. This novel feature may shed light on the pathologi-cal mechanisms of seizure generation and, in perspective, help develop new therapeutic strategies to manage refractory partial epilepsy.
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A compact smart sensor based on a neural classifier for objects modeled by Beaunier's model
PublicationA new solution of a smart microcontroller sensor based on a simple direct sensor-microcontroller interface for technical objects modeled by two-terminal networks and by the Beaunier’s model of anticorrosion coating is proposed. The tested object is stimulated by a square pulse and its time voltage response is sampled four times by the internal ADC of microcontroller. A neural classifier based on measurement data classifies the...
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PTD4 Peptide Increases Neural Viability in an In Vitro Model of Acute Ischemic Stroke
PublicationIschemic stroke is a disturbance in cerebral blood flow caused by brain tissue ischemia and hypoxia. We optimized a multifactorial in vitro model of acute ischemic stroke using rat primary neural cultures. This model was exploited to investigate the pro-viable activity of cell-penetrating peptides: arginine-rich Tat(49–57)-NH2 (R49KKRRQRRR57-amide) and its less basic analogue, PTD4 (Y47ARAAARQARA57-amide). Our model included glucose...
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Uniform Model Interface for Assurance Case Integration with System Models
PublicationAssurance cases are developed and maintained in parallel with corresponding system models and therefore need to reference each other. Managing the correctness and consistency of interrelated safety argument and system models is essential for system dependability and is a nontrivial task. The model interface presented in this paper enables a uniform process of establishing and managing assurance case references to various types...
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Spurious Modes in Model Order Reduction in Variational Problems in Electromagnetics
PublicationIn this work, we address an everlasting issue in 2 model order reduction (MOR) in electromagnetics that has 3 remained unnoticed until now. Contrary to what has been 4 previously done, we identify for the very first time spurious 5 modes in MOR for time-harmonic Maxwell’s equations and 6 propose a methodology to remove their negative influence on the 7 reduced order model (ROM) response. These spurious modes 8 have nonzero resonance...
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Numerical model of human head phantom to ensure dosimetry of dose components for boron neutron capture therapy
PublicationExtremely important aspects of the boron neutron capture therapy are, first of all, administering to the patient a boron compound that selectively reaches the neoplastic cells, and in the second step, the verification of the irradiation process. This paper focuses on the latter aspect, which is the detailed dosimetry of the processes occurring after the reaction of thermal neutrons with the boron-10 isotope. The results of computer...
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Expression-based decision tree model reveals distinct microRNA expression pattern in pediatric neuronal and mixed neuronal-glial tumors
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Neural networks based NARX models in nonlinear adaptive control
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Collective modes of the extended Hubbard model with negativeUand arbitrary electron density
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High speed milling vibration surveillance using modal model of the tool
PublicationPraca poświęcona jest nadzorowaniu drgań wirujących narzędzi w nowoczesnych frezarkach. Przedmiotem rozważań jest proces frezowania szybkościowego smukłym frezem kulistym na 5-osiowym centrum obróbkowym Deckel Maho DMU 50eVolution. Frezowanie smukłymi narzędziami jest często stosowane w przypadku nowoczesnych centrów obróbkowych. Uzasadnienie technologiczne wynika z konieczności dokładnego wykonywania złożonych kształtów geometrycznych...
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Identification of the Vistula Mounting tower model using measured modal data
PublicationW pracy przedstawiono problem identyfikacji pięcioparametrowego modelu zabytkowej wieży twierdzy Wisłoujście. Poszukiwane parametry wyznaczono jako rozwiązanie problemu minimalizacji błędu średniokwadratowego pomierzonych dwóch pierwszych częstości i pierwszej postaci drgań własnych. Do tego celu wykorzystano hierarchiczną procedurę minimalizacji przy zastosowaniu analizy wrażliwości. Analiza numeryczna potwierdziła efektywność...
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Extended Hopfield models of neural networks for combinatorial multiobjective optimization problems
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublicationThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
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Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model
PublicationThis work attempts to meet the challenges associated with the classification of LIDAR point clouds by means of deep learning. In addition to achieving high accuracy, the designed system should allow the classification of point clouds covering an area of several dozen square kilometers within a reasonable time interval. Therefore, it must be characterized by fast processing and efficient use of memory. Thus, the most popular approaches...
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From Weak to Strong Coupling Superconductivity: Collective Modes in a Model with Local Attraction
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Sensitivity of binomial N‐mixture models to overdispersion: The importance of assessing model fit
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The role and concept of sub-models in the smart fuzzy model of the internet mortgage market
PublicationThe paper introduces some challenges of the fast growing mortgage market in Poland. One of these challenges is the need for a model development that could be used for various predictions related to this market. At the current stage of the model evelopment process our main goal is to propose and introduce sub-models the role of which would be to describe three different economic environments: stable, fast growing, and recession....
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Derivation of Executable Test Models From Embedded System Models using Model Driven Architecture Artefacts - Automotive Domain
PublicationThe approach towards system engineering compliant to Model-Driven Architecture (MDA) implies an increased need for research on the automation of the model-based test generation. This applies especially to embedded real-time system development where safety critical requirements must be met by a system. The following paper presents a methodology to derive basic Simulink test models from Simulink system models so as to execute them...
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Towards bees detection on images: study of different color models for neural networks
PublicationThis paper presents an approach to bee detection in videostreams using a neural network classifier. We describe the motivationfor our research and the methodology of data acquisition. The maincontribution to this work is a comparison of different color models usedas an input format for a feedforward convolutional architecture appliedto bee detection. The detection process has is based on a neural...
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Comparison of Selected Neural Network Models Used for Automatic Liver Tumor Segmentation
PublicationAutomatic and accurate segmentation of liver tumors is crucial for the diagnosis and treatment of hepatocellular carcinoma or metastases. However, the task remains challenging due to imprecise boundaries and significant variations in the shape, size, and location of tumors. The present study focuses on tumor segmentation as a more critical aspect from a medical perspective, compared to liver parenchyma segmentation, which is the...
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Accuracy Investigations of Turbine Blading Neural Models Applied to Thermal and Flow Diagnostics
PublicationPossibility of replacing computional fluid dynamics simulations by a neural model for fluid flow and thermal diagnostics of steam turbines is investigated. Results of calculations of velocity magnitude of steam for 3D model of the stator of steam turbine is presented.
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Model Predictive Super-Twisting Sliding Mode Control for An Autonomous Surface Vehicle
PublicationThis paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynamical uncertainties. For fulfilling the robustness property, a sliding mode control-based procedure for designing of MPC and a super-twisting term are adopted. The MPC algorithm has been...
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Application of Illuminating Modes concept in modal expansion of elliptical resonator
PublicationIn the paper an IlluminatingModes (IM) approach as an extension of Spectral Domain Approach (SDA) is presented. IM concept is applied to the case of open microstrip planar elliptical resonator. A modal expansion of currents induced in the structures during illuminating them by a single plane wave is the novelty of the method. As an example of application of the method, we present the results obtained by an examination of two resonators...
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Design and experimental evaluation of pod propulsor models for a large self-propelled ship model.
PublicationArtykuł przedstawia serię hydrodynamicznych badań swobodnych dwóch modeli pędnika podowego. Pędniki zostały zaprojektowane i zbudowane specjalnie dla dwóch wersji dużego modelu okrętu z własnym napędem, przeznaczonego do eksperymentów manewrowych. Jedna wersja jest napędzana pojedyńczym pędnikiem, druga jest wyposażona w dwa pędniki. Oba modele podów były badane w kanale obiegowym. Celem eksperymentu były pomiary sześciu składowych...
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Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
PublicationThe present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases...
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Hybrid of Neural Networks and Hidden Markov Models as a modern approach to speech recognition systems
PublicationThe aim of this paper is to present a hybrid algorithm that combines the advantages ofartificial neural networks and hidden Markov models in speech recognition for control purpos-es. The scope of the paper includes review of currently used solutions, description and analysis of implementation of selected artificial neural network (NN) structures and hidden Markov mod-els (HMM). The main part of the paper consists of a description...
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Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks
PublicationObject detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction...