AIMC Topic: Markov Chains

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Resilient asynchronous state estimation of Markov switching neural networks: A hierarchical structure approach.

Neural networks : the official journal of the International Neural Network Society Dec 8, 2020
This paper deals with the issue of resilient asynchronous state estimation of discrete-time Markov switching neural networks. Randomly occurring signal quantization and packet dropout are involved in the imperfect measured output. The asynchronous sw...
Deep Learning Resilience, Psychological Markov Chains Neural Networks, Computer
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Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

IEEE transactions on medical imaging Nov 30, 2020
Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems be...
ROC Curve Markov Chains Normal Distribution Image Processing, Computer-Assisted Monte Carlo Method Supervised Machine Learning
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Synchronization of Coupled Time-Delay Neural Networks With Mode-Dependent Average Dwell Time Switching.

IEEE transactions on neural networks and learning systems Nov 30, 2020
In the literature, the effects of switching with average dwell time (ADT), Markovian switching, and intermittent coupling on stability and synchronization of dynamic systems have been extensively investigated. However, all of them are considered sepa...
Stochastic Processes Neural Networks, Computer Probability Markov Chains Algorithms
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Stochastic Finite-Time H State Estimation for Discrete-Time Semi-Markovian Jump Neural Networks With Time-Varying Delays.

IEEE transactions on neural networks and learning systems Nov 30, 2020
In this article, the finite-time H state estimation problem is addressed for a class of discrete-time neural networks with semi-Markovian jump parameters and time-varying delays. The focus is mainly on the design of a state estimator such that the co...
Algorithms Markov Chains Finite Element Analysis Stochastic Processes Neural Networks, Computer
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DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

eLife Nov 3, 2020
Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the...
Nanotechnology Markov Chains Kinetics Molecular Dynamics Simulation Single Molecule Imaging Normal Distribution Reproducibility of Results Fluorescence Resonance Energy Transfer Deep Learning False Positive Reactions Fluorescent Dyes Signal Processing, Computer-Assisted User-Computer Interface Software Algorithms
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Reward-predictive representations generalize across tasks in reinforcement learning.

PLoS computational biology Oct 15, 2020
In computer science, reinforcement learning is a powerful framework with which artificial agents can learn to maximize their performance for any given Markov decision process (MDP). Advances over the last decade, in combination with deep neural netwo...
Brain Neural Networks, Computer Humans Models, Psychological Animals Algorithms Markov Chains Reinforcement, Psychology Reward Task Performance and Analysis
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Learning molecular dynamics with simple language model built upon long short-term memory neural network.

Nature communications Oct 9, 2020
Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysi...
Memory Models, Statistical Single Molecule Imaging Kinetics Markov Chains Dipeptides Molecular Dynamics Simulation Neural Networks, Computer Artificial Intelligence Language
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Cyclic transitions between higher order motifs underlie sustained asynchronous spiking in sparse recurrent networks.

PLoS computational biology Sep 30, 2020
A basic-yet nontrivial-function which neocortical circuitry must satisfy is the ability to maintain stable spiking activity over time. Stable neocortical activity is asynchronous, critical, and low rate, and these features of spiking dynamics contrib...
Models, Neurological Computer Simulation Neural Networks, Computer Markov Chains Action Potentials Neurons Animals Neocortex Algorithms
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Exponential synchronization of neural networks with time-varying delays and stochastic impulses.

Neural networks : the official journal of the International Neural Network Society Sep 19, 2020
This paper concentrates on the exponential synchronization problem of the delayed neural networks (DNNs) with stochastic impulses. First, the impulsive Halanay differential inequality is further extended to the case that the impulsive strengths are r...
Markov Chains Neural Networks, Computer Stochastic Processes Time Factors
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Reverse-Engineering Neural Networks to Characterize Their Cost Functions.

Neural computation Sep 18, 2020
This letter considers a class of biologically plausible cost functions for neural networks, where the same cost function is minimized by both neural activity and plasticity. We show that such cost functions can be cast as a variational bound on model...
Markov Chains Humans Neural Networks, Computer Models, Theoretical Animals Models, Neurological
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