Relevant bibliographies by topics / Neural state-space models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Neural state-space models'

Author: Grafiati
Published: 19 April 2025

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Journal articles on the topic "Neural state-space models"

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Korbicz, Józef, Marcin Mrugalski, and Thomas Parisini. "DESIGNING STATE-SPACE MODELS WITH NEURAL NETWORKS." IFAC Proceedings Volumes 35, no. 1 (2002): 459–64. http://dx.doi.org/10.3182/20020721-6-es-1901.01630.

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Schüssler, Max. "Machine learning with nonlinear state space models." at - Automatisierungstechnik 70, no. 11 (October 27, 2022): 1027–28. http://dx.doi.org/10.1515/auto-2022-0089.

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Abstract In this dissertation, a novel class of model structures and associated training algorithms for building data-driven nonlinear state space models is developed. The new identification procedure with the resulting model is called local model state space network (LMSSN). Furthermore, recurrent neural networks (RNNs) and their similarities to nonlinear state space models are elaborated on. The overall outstanding performance of the LMSSN is demonstrated on various applications.
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He, Mingjian, Proloy Das, Gladia Hotan, and Patrick L. Purdon. "Switching state-space modeling of neural signal dynamics." PLOS Computational Biology 19, no. 8 (August 28, 2023): e1011395. http://dx.doi.org/10.1371/journal.pcbi.1011395.

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Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state-inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner.
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Forgione, Marco, and Dario Piga. "Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification." IFAC-PapersOnLine 56, no. 2 (2023): 4082–87. http://dx.doi.org/10.1016/j.ifacol.2023.10.1736.

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Raol, J. R. "Parameter estimation of state space models by recurrent neural networks." IEE Proceedings - Control Theory and Applications 142, no. 2 (March 1, 1995): 114–18. http://dx.doi.org/10.1049/ip-cta:19951733.

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Bendtsen, J. D., and K. Trangbaek. "Robust quasi-LPV control based on neural state-space models." IEEE Transactions on Neural Networks 13, no. 2 (March 2002): 355–68. http://dx.doi.org/10.1109/72.991421.

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Paninski, Liam, Yashar Ahmadian, Daniel Gil Ferreira, Shinsuke Koyama, Kamiar Rahnama Rad, Michael Vidne, Joshua Vogelstein, and Wei Wu. "A new look at state-space models for neural data." Journal of Computational Neuroscience 29, no. 1-2 (August 1, 2009): 107–26. http://dx.doi.org/10.1007/s10827-009-0179-x.

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Ghahramani, Zoubin, and Geoffrey E. Hinton. "Variational Learning for Switching State-Space Models." Neural Computation 12, no. 4 (April 1, 2000): 831–64. http://dx.doi.org/10.1162/089976600300015619.

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We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models—hidden Markov models and linear dynamical systems—and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.
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Aghaee, Mohammad, Stephane Krau, Melih Tamer, and Hector Budman. "Graph Neural Network Representation of State Space Models of Metabolic Pathways." IFAC-PapersOnLine 58, no. 14 (2024): 464–69. http://dx.doi.org/10.1016/j.ifacol.2024.08.380.

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Mangion, Andrew Zammit, Ke Yuan, Visakan Kadirkamanathan, Mahesan Niranjan, and Guido Sanguinetti. "Online Variational Inference for State-Space Models with Point-Process Observations." Neural Computation 23, no. 8 (August 2011): 1967–99. http://dx.doi.org/10.1162/neco_a_00156.

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We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.
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Dissertations / Theses on the topic "Neural state-space models"

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Beck, Amanda M. "State space models for isolating neural oscillations." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120408.

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Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 55-56).
Information communication in the brain depends on the spiking patterns of neurons. The interaction of these cells at the population level can be observed as oscillations of varying frequency and power, in local field potential recordings as well as non-invasive scalp electroencephalograms (EEG). These oscillations are thought to be responsible for coordinating activity across larger brain regions and conveying information across the brain, directing processes such as attention, consciousness, sensory and information processing. A common approach for analyzing these electrical potentials is to apply a band pass filter in the frequency band of interest. Canonical frequency bands have been defined and applied in many previous studies, but their specific definitions vary within the field, and are to some degree arbitrary. We propose an alternative approach that uses state space models to represent basic physiological and dynamic principles, whose detailed structure and parameterization are informed by observed data. We find that this method can more accurately represent oscillatory power, effectively separating it from background broadband noise power. This approach provides a way of separating oscillations in the time domain and while also quantifying their structure efficiently with a small number of parameters.
by Amanda M. Beck.
S.M. in Computer Science and Engineering
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Hache, Alexandre. "Modélisation et commande de systèmes non-linéaires par apprentissage sous contraintes SDP de réseaux de neurones paramétrés." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2025. http://www.theses.fr/2025IMTA0458.

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Cette thèse s’inscrit au croisement entre les théories de l’apprentissage et de la commande, proposant une méthodologie basée données, pour la modélisation et le contrôle des systèmes dynamiques nonlinéaires. En s’appuyant sur la théorie de la stabilité absolue et sur une représentation générale des modèles d’état neuronaux, plusieurs théorèmes de stabilité pour les réseaux de neurones sont présentés. Face aux limitations des approches traditionnelles d’optimisation sous contraintes LMI, nous développons un cadre théorique complet pour la paramétrisation des réseaux de neurones, compatible avec les algorithmes de gradient et les outils de différentiation automatique classiques. A l’aide de la théorie sur la linéarisation par bouclage, l’apprentissage en une seule étape d’un contrôleur approximativement linéarisant et d’un modèle de référence aux propriétés de stabilité garanties est présentée. Les résultats théoriques sont validés sur des exemples académiques d’atténuation de perturbations, ouvrant la voie à une utilisation plus systématique des réseaux de neurones dans la conception de lois de commande
This thesis lies at the crossroad between learning theory and control theory, proposing a data-driven methodology for modeling and controlling nonlinear dynamical systems. Drawing from the absolute stability theory,and from a general representation of neural state-space models, several stability theorems are presented. Facing the limitations of traditional optimization approaches under LMI constraints for neural networks, we develop a complete theoretical framework for neural network parameterization, compatible with gradient algorithms and classical automatic differentiation tools. With the help of feedback linearization theory, a single-step learning method of an approximately linearizing controller and a reference model with guaranteed stability properties is presented. The theoretical results are validated on academic examples of disturbance attenuation, paving the way for more systematic use of neural networks in controllers’ design
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Ogunc, Fethi. "Estimating The Neutral Real Interest Rate For Turkey By Using An Unobserved Components Model." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607426/index.pdf.

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In this study, neutral real interest rate gap and output gap are estimated jointly under two different multivariate unobserved components models with the motivation to provide empirical measures that can be used to analyze the amount of stimulus that monetary policy is passing on to the economy, and to understand historical macroeconomic developments. In the analyses, Kalman filter technique is applied to a small-scale macroeconomic model of the Turkish economy to estimate the unobserved variables for the period 1989-2005. In addition, two alternative specifications for neutral real interest rate are used in the analyses. The first model uses a random walk model for the neutral real interest rate, whereas the second one employs more structural specification, which specifically links the neutral real rate with the trend growth rate and the long-term course of the risk premium. Comparison of the models developed by using various performance criteria clearly indicates the use of more structural specification against random walk specification. Results suggest that though there is relatively high uncertainty surrounding the neutral real interest rate estimates to use them directly in the policy-making process, estimates appear to be very useful for ex-post monetary policy evaluations.
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Rodrigues, Júnior Selmo Eduardo. "Metodologia evolutiva para previsão inteligente de séries temporais sazonais baseada em espaço de estados não-observáveis." Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1723.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-07-03T18:32:31Z No. of bitstreams: 1 SelmoRodrigues.pdf: 1374245 bytes, checksum: 96afcfa04ba5cc18c4db55e4c92cdf23 (MD5)
Made available in DSpace on 2017-07-03T18:32:31Z (GMT). No. of bitstreams: 1 SelmoRodrigues.pdf: 1374245 bytes, checksum: 96afcfa04ba5cc18c4db55e4c92cdf23 (MD5) Previous issue date: 2017-01-26
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
This paper proposes a new methodology for modelling based on an evolving Neuro-Fuzzy Network Takagi-Sugeno (NFN-TS) for seasonal time series forecasting. The NFN-TS use the unobservable components extracted from the time series to evolve, i.e., to adapt and to adjust its structure, where the number of fuzzy rules of this network can increase or reduced according the components behavior. The method used to extract the components is a recursive version developed in this paper based on the Spectral Singular Analysis (SSA) technique. The proposed methodology has the principle divide to conquer, i.e., it divides a problem into easier subproblems, forecasting separately each component because they present dynamic behaviors that are simpler to forecast. The consequent propositions of fuzzy rules are linear state space models, where the states are the unobservable components data. When there are available observations from the time series, the training stage of NFN-TS is performed, i.e., the NFN-TS evolves its structure and adapts its parameters to carry out the mapping between the components data and the available sample of original time series. On the other hand, if this observation is not available, the network considers the forecasting stage, keeping its structure fixed and using the states of consequent fuzzy rules to feedback the components data to NFN-TS. The NFN-TS was evaluated and compared with other recent and traditional techniques for forecasting seasonal time series, obtaining competitive and advantageous results in relation to other papers. This paper also presents a case study of proposed methodology for real-time detection of anomalies based on a patient’s electrocardiogram data.
Esse trabalho propõe uma nova metodologia para modelagem baseada em uma Rede Neuro- Fuzzy Takagi-Sugeno (RNF-TS) evolutiva para a previsão de séries temporais sazonais. A RNF-TS considera as componentes não-observáveis extraídas a partir da série para evoluir, ou seja, adaptar e ajustar sua estrutura, sendo que a quantidade de regras fuzzy dessa rede pode aumentar ou ser reduzida conforme o comportamento das componentes. O método utilizado para extrair as componentes é uma versão recursiva desenvolvida nessa pesquisa baseada na técnica de Análise Espectral Singular (AES). A metodologia proposta tem como princípio dividir para conquistar, isto é, dividir um problema em subproblemas mais fáceis de lidar, realizando a previsão separadamente de cada componente já que apresentam comportamentos dinâmicos mais simples de prever. As proposições do consequente das regras fuzzy são modelos lineares no espaço de estados, sendo que os estados são os próprios dados das componentes não-observáveis. Quando há observações disponíveis da série temporal, o estágio de treinamento da RNF-TS é realizado, ou seja, a RNF-TS evolui sua estrutura e adapta seus parâmetros para realizar o mapeamento entre os dados das componentes e a amostra disponível da série temporal original. Caso contrário, se essa observação não está disponível, a rede aciona o estágio de previsão, mantendo sua estrutura fixa e usando os estados dos consequentes das regras fuzzy para realimentar os dados das componentes para a RNF-TS. A RNF-TS foi avaliada e comparada com outras técnicas recentes e tradicionais para previsão de séries temporais sazonais, obtendo resultados competitivos e vantajosos em relação a outras pesquisas. Este trabalho apresenta também um estudo de caso da metodologia proposta para detecção em tempo-real de anomalias baseada em dados de eletrocardiogramas de um paciente.
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Vidne, Michael. "State-Space Models and Latent Processes in the Statistical Analysis of Neural Data." Thesis, 2011. https://doi.org/10.7916/D88058JW.

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This thesis develops and applies statistical methods for the analysis of neural data. In the second chapter we incorporate a latent process to the Generalized Linear Model framework. We develop and apply our framework to estimate the linear filters of an entire population of retinal ganglion cells while taking into account the effects of common-noise the cells might share. We are able to capture the encoding and decoding of visual stimulus to neural code. Our formalism gives us insight into the underlying architecture of the neural system. And we are able to estimate the common-noise that the cells receive. In the third chapter we discuss methods for optimally inferring the synaptic inputs to an electrotonically compact neuron, given intracellular voltage-clamp or current-clamp recordings from the postsynaptic cell. These methods are based on sequential Monte Carlo techniques ("particle filtering"). We demonstrate, on model data, that these methods can recover the time course of excitatory and inhibitory synaptic inputs accurately on a single trial. In the fourth chapter we develop a more general approach to the state-space filtering problem. Our method solves the same recursive set of Markovian filter equations as the particle filter, but we replace all importance sampling steps with a more general Markov chain Monte Carlo (MCMC) step. Our algorithm is especially well suited for problems where the model parameters might be misspecified.
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Deng, Xinyi. "Point process modeling and estimation: advances in the analysis of dynamic neural spiking data." Thesis, 2016. https://hdl.handle.net/2144/17719.

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A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments.
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Tao, Long. "Contributions to statistical analysis methods for neural spiking activity." Thesis, 2018. https://hdl.handle.net/2144/33172.

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With the technical advances in neuroscience experiments in the past few decades, we have seen a massive expansion in our ability to record neural activity. These advances enable neuroscientists to analyze more complex neural coding and communication properties, and at the same time, raise new challenges for analyzing neural spiking data, which keeps growing in scale, dimension, and complexity. This thesis proposes several new statistical methods that advance statistical analysis approaches for neural spiking data, including sequential Monte Carlo (SMC) methods for efficient estimation of neural dynamics from membrane potential threshold crossings, state-space models using multimodal observation processes, and goodness-of-fit analysis methods for neural marked point process models. In a first project, we derive a set of iterative formulas that enable us to simulate trajectories from stochastic, dynamic neural spiking models that are consistent with a set of spike time observations. We develop a SMC method to simultaneously estimate the parameters of the model and the unobserved dynamic variables from spike train data. We investigate the performance of this approach on a leaky integrate-and-fire model. In another project, we define a semi-latent state-space model to estimate information related to the phenomenon of hippocampal replay. Replay is a recently discovered phenomenon where patterns of hippocampal spiking activity that typically occur during exploration of an environment are reactivated when an animal is at rest. This reactivation is accompanied by high frequency oscillations in hippocampal local field potentials. However, methods to define replay mathematically remain undeveloped. In this project, we construct a novel state-space model that enables us to identify whether replay is occurring, and if so to estimate the movement trajectories consistent with the observed neural activity, and to categorize the content of each event. The state-space model integrates information from the spiking activity from the hippocampal population, the rhythms in the local field potential, and the rat's movement behavior. Finally, we develop a new, general time-rescaling theorem for marked point processes, and use this to develop a general goodness-of-fit framework for neural population spiking models. We investigate this approach through simulation and a real data application.
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Bartoš, Samuel. "Predikce profilů spotřeby elektrické energie." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-365100.

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Prediction of energy load profiles is an important topic in Smart Grid technologies. Accurate forecasts can lead to reduced costs and decreased dependency on commercial power suppliers by adapting to prices on energy market, efficient utilisation of solar and wind energy and sophisticated load scheduling. This thesis compares various statistical and machine learning models and their ability to forecast load profile for an entire day divided into 48 half-hour intervals. Additionally, we examine various preprocessing methods and their influence on the accuracy of the models. We also compare a variety of imputation methods that are designed to reconstruct missing observation commonly present in energy consumption data.
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Books on the topic "Neural state-space models"

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Vidne, Michael. State-Space Models and Latent Processes in the Statistical Analysis of Neural Data. [New York, N.Y.?]: [publisher not identified], 2011.

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Chen, Zhe. Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press, 2015.

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Chen, Zhe. Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press, 2015.

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Chen, Zhe. Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press, 2015.

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MARQUÉS, Felicidad. AUTOMATIC TIME SERIES FORECASTING Using NEURAL NETWORKS, STATE SPACE and ARIMAX MODELS. Examples with R. Independently Published, 2021.

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A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding. Providence, USA: Brown University, 2019.

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Ashby, F. Gregory, and Fabian A. Soto. Multidimensional Signal Detection Theory. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.2.

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Multidimensional signal detection theory is a multivariate extension of signal detection theory that makes two fundamental assumptions, namely that every mental state is noisy and that every action requires a decision. The most widely studied version is known as general recognition theory (GRT). General recognition theory assumes that the percept on each trial can be modeled as a random sample from a multivariate probability distribution defined over the perceptual space. Decision bounds divide this space into regions that are each associated with a response alternative. General recognition theory rigorously defines and tests a number of important perceptual and cognitive conditions, including perceptual and decisional separability and perceptual independence. General recognition theory has been used to analyze data from identification experiments in two ways: (1) fitting and comparing models that make different assumptions about perceptual and decisional processing, and (2) testing assumptions by computing summary statistics and checking whether these satisfy certain conditions. Much has been learned recently about the neural networks that mediate the perceptual and decisional processing modeled by GRT, and this knowledge can be used to improve the design of experiments where a GRT analysis is anticipated.
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Butz, Martin V., and Esther F. Kutter. Top-Down Predictions Determine Perceptions. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0009.

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While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.
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Nolte, David D. Introduction to Modern Dynamics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844624.001.0001.

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Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase space, whose geometric properties define the set of all trajectories through those spaces. The section on nonlinear dynamics has chapters on chaos theory, synchronization, and networks. Chaos theory provides the language and tools to understand nonlinear systems, introducing fixed points that are classified through stability analysis and nullclines that shepherd system trajectories. Synchronization and networks are central paradigms in this book because they demonstrate how collective behavior emerges from the interactions of many individual nonlinear elements. The section on complex systems contains chapters on neural dynamics, evolutionary dynamics, and economic dynamics. The final section contains chapters on metric spaces and the special and general theories of relativity. In the second edition, sections on conventional topics, like applications of Lagrangians, have been strengthened, as well as being updated to provide a modern perspective. Several of the introductory chapters have been rearranged for improved logical flow and there are expanded homework problems at the end of each chapter.
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Mittelbach, Gary G., and Brian J. McGill. Community Ecology. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198835851.001.0001.

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Community Ecology provides a broad, up-to-date coverage of ecological concepts at the community level and is suitable for advanced undergraduates, graduate students, and ecological researchers. The field of community ecology has undergone a transformation in recent years, from a discipline largely focused on processes occurring within a local area to a discipline encompassing a much richer domain of study, including the linkages between communities separated in space (metacommunity dynamics), niche and neutral theory, the interplay between ecology and evolution (eco-evolutionary dynamics), and the influence of historical and regional processes in shaping patterns of biodiversity. To fully understand these new developments, however, students continue to need a strong foundation in the study of species interactions, and how these interactions are assembled into community modules and ecological networks. Trait-based assembly rules are presented as another approach to understanding community assembly, especially for real-world communities that may contain hundreds of species. This new edition fulfils the book’s original aims, both as a much-needed up-to-date and accessible introduction to modern community ecology, and in identifying the important questions that are yet to be answered. This research-driven textbook introduces state-of-the-art community ecology to a new generation of students, adopting reasoned and balanced perspectives on as-yet-unresolved issues. Pictures and graphics throughout the text allow students to visualize advanced concepts.
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Book chapters on the topic "Neural state-space models"

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Ławryńczuk, Maciej. "MPC Algorithms Based on Neural State-Space Models." In Studies in Systems, Decision and Control, 139–66. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04229-9_4.

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Liitiäinen, Elia, and Amaury Lendasse. "Long-Term Prediction of Time Series Using State-Space Models." In Artificial Neural Networks – ICANN 2006, 181–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_19.

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Wang, Fan, Keli Wang, and Boyu Yao. "Time Series Anomaly Detection with Reconstruction-Based State-Space Models." In Artificial Neural Networks and Machine Learning – ICANN 2023, 74–86. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44213-1_7.

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Eden, Uri T., Loren M. Frank, and Long Tao. "Characterizing Complex, Multi-Scale Neural Phenomena Using State-Space Models." In Dynamic Neuroscience, 29–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71976-4_2.

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Kung, S. Y., and J. N. Huang. "Systolic Designs for State Space Models: Kalman Filtering and Neural Networks." In Concurrent Computations, 619–43. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-5511-3_31.

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Ławryńczuk, Maciej. "Computationally Efficient Nonlinear Predictive Control Based on State-Space Neural Models." In Parallel Processing and Applied Mathematics, 350–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14390-8_36.

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Mphale, Ofaletse, and V. Lakshmi Narasimhan. "Comparative Forecasts of Confirmed COVID-19 Cases in Botswana Using Box-Jenkin's ARIMA and Exponential Smoothing State-Space Models." In Recurrent Neural Networks, 355–81. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822-23.

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Rigatos, Gerasimos G. "Validation of Financial Options Models Using Neural Networks with Invariance to Fourier Transform." In State-Space Approaches for Modelling and Control in Financial Engineering, 167–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52866-3_9.

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Chen, Zhe, and Emery N. Brown. "State-Space Models for the Analysis of Neural Spike Train and Behavioral Data." In Encyclopedia of Computational Neuroscience, 2864–67. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_410.

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Chen, Zhe, and Emery N. Brown. "State-Space Models for the Analysis of Neural Spike Train and Behavioral Data." In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_410-1.

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Conference papers on the topic "Neural state-space models"

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Fan, Yiming, Peiyuan Zhou, David Forrester, Brian Ju, and Fotis Kopsaftopoulos. "Evaluation of Local and Global Diagnostics for the Integration of Stochastic Time Series Models and Variational Autoencoders: Experimental Assessment on a Full Scale Helicopter Blade." In Vertical Flight Society 80th Annual Forum & Technology Display, 1–10. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1371.

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In this work, a unified framework integrating global and local SHM methods for structural health monitoring (SHM) of rotorcraft structures is proposed. This framework integrates both "local" ultrasonic-guided wave-based and "global" vibration-based SHM schemes for tackling damage detection, identification, and quantification under uncertainty. The local SHM is completed by training a variation of variational auto-encoder (MMD-VAE) along with feed-forward neural networks (FFNN). The compressed latent space vector obtained during the training process is applied to achieve both signal reconstruction and state prediction. In terms of the global model, functionally pooled auto-regressive models with exogenous excitation (VFP-ARX) models are applied including to capture low-frequency vibrations. The complete experimental evaluation and assessment of the proposed framework are presented for an Airbus H125 helicopter blade under both low-frequency vibrations and ultrasonic guided waves for SHM.
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Deng, Weihao, Fei Han, Qinghua Ling, Qing Liu, and Henry Han. "Causal fMRI-Mamba: Causal State Space Model for Neural Decoding and Brain Task States Recognition." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889151.

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Zhu, YanZong, Yuesong Yang, Feng Li, and Zhou Zhou. "Identification Modelling of the Hammerstein Nonlinear Systems Utilizing Adaptive Neural Fuzzy Networks and State Space Model." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS), 1494–98. IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606730.

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Murakami, Ryo, Satoshi Mori, and Haichong K. Zhang. "Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2373–79. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801769.

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Aghagolzadeh, Mehdi, and Wilson Truccolo. "Latent state-space models for neural decoding." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944262.

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Beck, Amanda M., Emily P. Stephen, and Patrick L. Purdon. "State Space Oscillator Models for Neural Data Analysis." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513215.

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Bendtsen, Jan. "A Right Coprime Factorization of Neural State Space Models." In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). IEEE, 2007. http://dx.doi.org/10.1109/isda.2007.111.

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Bendtsen, Jan. "A Right Coprime Factorization of Neural State Space Models." In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). IEEE, 2007. http://dx.doi.org/10.1109/isda.2007.4389605.

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Grigorievskiy, Alexander, and Juha Karhunen. "Gaussian Process kernels for popular state-space time series models." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727628.

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Song, Christian Y., Han-Lin Hsieh, and Maryam M. Shanechi. "Decoder for Switching State-Space Models with Spike-Field Observations." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2019. http://dx.doi.org/10.1109/ner.2019.8716970.

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Reports on the topic "Neural state-space models"

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Pavlyuk, Іhor. Культурно-інформаційний простір України в роки німецько-фашистської окупації: за матеріалами україномовної колаборантської преси. Ivan Franko National University of Lviv, March 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11719.

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The purpose of thіs artіcle іs to cover the cultural and іnformatіon space of the western Ukraіnіan lands durіng the Nazі occupatіon: accordіng to the Ukraіnіan-language collaboratіng press іn the context of exіstentіal projectіons on the modern war іn Ukraіne wіth Russіa’s occupatіon of some Ukraіnіan terrіtorіes. The methodologіcal basіs of our study іs the groupіng and іnductіve-deductіve analysіs of the then medіa (іncludіng the press) by place of publіcatіon and genre-thematіc focus (perіodіcals for women, chіldren’s magazіnes, busіness newspapers and magazіnes), the separatіon of іnformatіon-analytіcal neutral and the propaganda paradіgm wіth pro-Ukraіnіan and pro-German, antі-Bolshevіk socіo-polіtіcal vectors: dіstіnguіshіng between “Ukraіnіan-language” and “Ukraіnіan-language” journalіsm, whіch іn the mass medіa turn the press іnto a metatext whose modalіty can be useful and constructіve. (state-buіldіng) and negatіve (destructіve) patterns of functіonіng of the medіa іn the enemy-occupіed terrіtory, when іt іs necessary to fіght on several fronts at the same tіme. Among the research methods used іn the artіcle: comparatіve, phenomenologіcal, psychoanalytіc (probіng archetypes), hermeneutіc, deconstructіvіst, socіo-psychologіcal. The study showed and confіrmed that one of the best іllustratіons of German polіcy іn Ukraіne durіng World War ІІ was the attіtude of the occupіer to relіgіon, Ukraіnіan women, chіldren, and other occupіers, іncludіng the Bolshevіks, as reflected іn the eponymous Ukraіnіan magazіnes (“Ukraіnіan chіld”, “Farmer”, etc.) and, of course, іn theіr content and even formal desіgn, as stated іn the text of the artіcle The obtaіned results allowed us to formulate the followіng conclusіons. An analysіs of the Ukraіnіan-language (collaboratіng) press publіshed іn the western part of Ukraіne іn 1941-1944 convіncіngly proves that only an іndependent, sovereіgn state can claіm authentіcally, deeply іts own, іdentіcal mass medіa. And controlled, because the medіa fіnanced by the occupatіon authorіtіes, although publіshed іn Ukraіnіan, were Ukraіnіan-speakіng іn letter, but German-speakіng іn spіrіt, іe not Ukraіnіan-speakіng, although well-known Ukraіnіan artіsts took part іn the creatіon of these propagandіstіc sources of іnformatіon. sіgnіfіcant names and archetypes of Ukraіnіan culture were engaged at that tіme. Key words: collaboratіng press, propaganda, іdentіty, mass medіa, cultural and іnformatіon space.
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BARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.

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The auroral activity indices AU, AL, AE, introduced into geophysics at the beginning of the space era, although they have certain drawbacks, are still widely used to monitor geomagnetic activity at high latitudes. The AU index reflects the intensity of the eastern electric jet, while the AL index is determined by the intensity of the western electric jet. There are many regression relationships linking the indices of magnetic activity with a wide range of phenomena observed in the Earth's magnetosphere and atmosphere. These relationships determine the importance of monitoring and predicting geomagnetic activity for research in various areas of solar-terrestrial physics. The most dramatic phenomena in the magnetosphere and high-latitude ionosphere occur during periods of magnetospheric substorms, a sensitive indicator of which is the time variation and value of the AL index. Currently, AL index forecasting is carried out by various methods using both dynamic systems and artificial intelligence. Forecasting is based on the close relationship between the state of the magnetosphere and the parameters of the solar wind and the interplanetary magnetic field (IMF). This application proposes an algorithm for describing the process of substorm formation using an instrument in the form of an Elman-type ANN by reconstructing the AL index using the dynamics of the new integral parameter we introduced. The use of an integral parameter at the input of the ANN makes it possible to simulate the structure and intellectual properties of the biological nervous system, since in this way an additional realization of the memory of the prehistory of the modeled process is provided.
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