scholarly journals Evidence accumulation and change rate inference in dynamic environments

2016 ◽  
Author(s):  
Adrian E Radillo ◽  
Alan Veliz-Cuba ◽  
Kresimir Josic ◽  
Zachary Kilpatrick
Keyword(s):  
Rate Of Change ◽  
Ideal Observer ◽  
Moment Closure ◽  
Dimensional System ◽  
Change Rate ◽  
Observer Model ◽  
Low Dimensional ◽  
State Of The Environment ◽  
Rate Correlation ◽  
Moment Closure Approximation

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is updating the posterior probability of all possible changepoint counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation based plasticity rule. We thus show how optimal observers accumulates evidence in changing environments, and map this computation to reduced models which perform inference using plausible neural mechanisms.

scholarly journals Evidence Accumulation and Change Rate Inference in Dynamic Environments

2017 ◽  
Vol 29 (6) ◽  
pp. 1561-1610 ◽  
Author(s):  
Adrian E. Radillo ◽  
Alan Veliz-Cuba ◽  
Krešimir Josić ◽  
Zachary P. Kilpatrick
Keyword(s):  
Rate Of Change ◽  
Ideal Observer ◽  
Moment Closure ◽  
Dimensional System ◽  
Change Rate ◽  
Point Counts ◽  
Observer Model ◽  
Low Dimensional ◽  
State Of The Environment ◽  
Rate Correlation

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

scholarly journals How Optimal is Word-Referent Identification Under Multimodal Uncertainty?

2018 ◽  
Author(s):  
Abdellah Fourtassi ◽  
Michael C. Frank
Keyword(s):  
Visual Cues ◽  
Word Meaning ◽  
Spoken Word ◽  
Ideal Observer ◽  
Optimal Model ◽  
Sources Of Uncertainty ◽  
Multimodal Information ◽  
Model Predictions ◽  
Observer Model ◽  
Novel Words

Identifying a spoken word in a referential context requires both the ability to integrate multimodal input and the ability to reason under uncertainty. How do these tasks interact with one another? We study how adults identify novel words under joint uncertainty in the auditory and visual modalities and we propose an ideal observer model of how cues in these modalities are combined optimally. Model predictions are tested in four experiments where recognition is made under various sources of uncertainty. We found that participants use both auditory and visual cues to recognize novel words. When the signal is not distorted with environmental noise, participants weight the auditory and visual cues optimally, that is, according to the relative reliability of each modality. In contrast, when one modality has noise added to it, human perceivers systematically prefer the unperturbed modality to a greater extent than the optimal model does. This work extends the literature on perceptual cue combination to the case of word recognition in a referential context. In addition, this context offers a link to the study of multimodal information in word meaning learning.

Large-eddy simulation of direct injection natural gas combustion in a heavy-duty truck engine using modified conditional moment closure model with low-dimensional manifold method

2018 ◽  
Vol 21 (5) ◽  
pp. 824-837 ◽  
Author(s):  
Jian Huang ◽  
Gordon McTaggart-Cowan ◽  
Sandeep Munshi
Keyword(s):  
Natural Gas ◽  
Direct Injection ◽  
Moment Closure ◽  
Dimensional Manifold ◽  
Conditional Moment ◽  
Gas Combustion ◽  
Conditional Moment Closure ◽  
Natural Gas Combustion ◽  
Low Dimensional ◽  
Low Dimensional Manifold

This article describes the application of a modified first-order conditional moment closure model used in conjunction with the trajectory-generated low-dimensional manifold method in large-eddy simulation of pilot ignited high-pressure direct injection natural gas combustion in a heavy-duty diesel engine. The article starts with a review of the intrinsic low-dimensional manifold method for reducing detailed chemistry and various formulations for the construction of such manifolds. It is followed by a brief review of the conditional moment closure method for modelling the interaction between turbulence and combustion chemistry. The high computational cost associated with the direct implementation of the basic conditional moment closure model was discussed. The article then describes the formulation of a modified approach to solve the conditional moment closure equation, whose reaction source terms for the conditional mass fractions for species were obtained by projecting the turbulent perturbation onto the reaction manifold. The main model assumptions were explained and the resulting limitations were discussed. A numerical experiment was conducted to examine the validity the model assumptions. The model was then implemented in a combustion computational fluid dynamics solver developed on an open-source computational fluid dynamics platform. Non-reactive jet simulations were first conducted and the results were compared to the experimental measurement from a high-pressure visualization chamber to verify that the jet penetration under engine relevant conditions was correctly predicted. The model was then used to simulate natural gas combustion in a heavy-duty diesel engine equipped with a high-pressure direct injection system. The simulation results were compared with the experimental measurement from a research engine to verify the accuracy of the model for both the combustion rate and engine-out emissions.

Non-Standard Reduction of Noisy Structural Systems: Shallow Arches

2000 ◽  
Author(s):  
Lalit Vedula ◽  
N. Sri Namachchivaya
Keyword(s):  
Markov Process ◽  
Random Perturbation ◽  
Exit Time ◽  
Stochastic Averaging ◽  
Dimensional System ◽  
Shallow Arches ◽  
Higher Dimensional ◽  
Mean Exit Time ◽  
Stationary Probability Density Function ◽  
Low Dimensional

Abstract The dynamics of a shallow arch subjected to small random external and parametric excitation is invegistated in this work. We develop rigorous methods to replace, in some limiting regime, the original higher dimensional system of equations by a simpler, constructive and rational approximation – a low-dimensional model of the dynamical system. To this end, we study the equations as a random perturbation of a two-dimensional Hamiltonian system. We achieve the model-reduction through stochastic averaging and the reduced Markov process takes its values on a graph with certain glueing conditions at the vertex of the graph. Examination of the reduced Markov process on the graph yields many important results such as mean exit time, stationary probability density function.

scholarly journals Observable for a Large System of Globally Coupled Excitable Units

2019 ◽  
Vol 24 (2) ◽  
pp. 37 ◽  
Author(s):  
Santiago Boari ◽  
Gonzalo Uribarri ◽  
Ana Amador ◽  
Gabriel Mindlin
Keyword(s):  
Order Parameter ◽  
Field Potential ◽  
Dimensional System ◽  
System Of Equations ◽  
In Vivo Electrophysiology ◽  
Excitable Systems ◽  
Global Variable ◽  
Good Proxy ◽  
Low Dimensional

The study of large arrays of coupled excitable systems has largely benefited from a technique proposed by Ott and Antonsen, which results in a low dimensional system of equations for the system’s order parameter. In this work, we show how to explicitly introduce a variable describing the global synaptic activation of the network into these family of models. This global variable is built by adding realistic synaptic time traces. We propose that this variable can, under certain conditions, be a good proxy for the local field potential of the network. We report experimental, in vivo, electrophysiology data supporting this claim.

Hopf Bifurcation Analysis and Stability for a Ratio-Dependent Predator–Prey Diffusive System with Time Delay

2020 ◽  
Vol 30 (03) ◽  
pp. 2050037
Author(s):  
Longyue Li ◽  
Yingying Mei ◽  
Jianzhi Cao
Keyword(s):  
Hopf Bifurcation ◽  
Time Delay ◽  
Periodic Solutions ◽  
Positive Equilibrium ◽  
Dimensional System ◽  
Normal Form Theory ◽  
Center Manifold Reduction ◽  
Predator Prey ◽  
Ratio Dependent ◽  
Low Dimensional

In this paper, we are focused on a new ratio-dependent predator–prey system that introduced the diffusive and time delay effect simultaneously. By analyzing the characteristic equations and the distribution of eigenvalues, we examine the stability and boundary of positive equilibrium states, and the existence of spatially homogeneous and spatially inhomogeneous bifurcating periodic solutions, respectively. Further, we prove that when [Formula: see text], the system has Hopf bifurcation at the positive equilibrium state. By using the center manifold reduction, we simplify the system so that we can convert an infinite-dimensional system into a low-dimensional finite-dimensional system. By using the normal form theory, we obtain explicit expressions for the direction, stability and period of Hopf bifurcation periodic solutions. Finally, we have illustrated the main results in this thesis by numerical examples, our work may provide some useful measures to save time or cost and to control the ecosystem.

Sign in / Sign up

Export Citation Format

Share Document