The Ornstein–Uhlenbeck process as a model of a low pass filtered white noise

Bibbona, Enrico; Panfilo, G.; Tavella, P.

doi:10.1088/0026-1394/45/6/s17

Cited by 84 publications

(52 citation statements)

References 13 publications

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“…Since the CBC panel is typically ordered once or just a few times a day, we used the default Gaussian process window size of seven days. The covariance function is the combination of the mean reverting and the periodic functions( [16]). The mean reverting function forces the process to approach the long term mean, but at the same time permits temporary deviations from the mean corresponding to episodic events or complications.…”

Section: Resultsmentioning

confidence: 99%

Clinical Time Series Prediction with a Hierarchical Dynamical System

Liu

Hauskrecht

2013

Artificial Intelligence in Medicine

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Abstract. In this work we develop and test a novel hierarchical framework for modeling and learning multivariate clinical time series data. Our framework combines two modeling approaches: Linear Dynamical Systems (LDS) and Gaussian Processes (GP), and is capable to model and work with time series of varied length and with irregularly sampled observations. We test our framework on the problem of learning clinical time series data from the complete blood count panel, and show that our framework outperforms alternative time series models in terms of its predictive accuracy.

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Section: Resultsmentioning

confidence: 99%

Clinical Time Series Prediction with a Hierarchical Dynamical System

Liu

Hauskrecht

2013

Artificial Intelligence in Medicine

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“…However, if ∆t is comparable to or larger than the instantaneous time constant of the circuit, then the circuit node voltage will relax back to its original value between pulses and this is not physically accurate. This situation is well described by the Ornstein-Uhlenbeck process [2].…”

Section: Ornstein-uhlenbeck Process -The Effect Of Response Time On Nmentioning

confidence: 95%

A Simulation Framework for Analyzing Transient Effects Due to Thermal Noise in Sub-Threshold Circuits

Donato

Bahar

Patterson

et al. 2015

Proceedings of the 25th Edition on Great Lakes Symposium on VLSI

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Noise analysis in nonlinear logic circuits requires models that take into account time-varying biasing conditions. When considering thermal noise, which moves the circuit away from its equilibrium point, a correct modeling approach has to go beyond the additive white Gaussian noise (AWGN) used in classical noise analysis. Even when accurate models are available, running standard MonteCarlo simulations that will expose rare soft errors may still be computationally prohibitive. Probabilistic methods are often preferred for estimating the failure rate. However, these approaches may not provide any insight about the dynamic response to noise events. In this paper, we target both problems in the sub-threshold logic application domain. We first provide a time-domain model for fundamental, technology-independent thermal noise in sub-threshold circuits. Then, we use this model to generate noise input files for SPICE transient analysis. The effectiveness of the approach is demonstrated using 7nm FinFET predictive technology models (PTM) for an inverter and a NAND gate.

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“…One way of doing it was implementing the Ornstein-Uhlenbeck process (OU-process), which is a stochastic process that allowed us to simulate small random variability. The OU-process describes the velocity of the movement of a Brownian particle considering the friction and is a stationary Gauss-Markov process (Enrico Bibbona, 2008).…”

Section: Noise Calibration Ornstein-uhlenbeck Processmentioning

confidence: 99%

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

Barros-Zulaica

Rahmon

Chindemi

et al. 2019

Preprint

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Previous studies based on the 'Quantal Model' for synaptic transmission suggested that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily-releasable pool (NRRP), from paired whole-cell recordings of layer 5 thick tufted pyramidal cell (L5_TTPC) connections in the somatosensory neocortex. Our approach extends the work of Loebel and colleagues to take advantage of a recently reported data-driven biophysical model of neocortical tissue. Using this approach, we estimated NRRP to be between two to three for connections between L5-TTPC. To constrain NRRP values for other connections in the microcircuit, we developed and validated a generalization approach using data on post-synaptic potential (PSP) coefficient of variations (CVs) from literature and matching to in silico experiments. Our study shows that synaptic connections in the neocortex generally are mediated by MVR and provides a data-driven approach to constrain the MVR model parameters of the microcircuit.

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The Ornstein–Uhlenbeck process as a model of a low pass filtered white noise

Cited by 84 publications

References 13 publications

Clinical Time Series Prediction with a Hierarchical Dynamical System

Clinical Time Series Prediction with a Hierarchical Dynamical System

A Simulation Framework for Analyzing Transient Effects Due to Thermal Noise in Sub-Threshold Circuits

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

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