Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience
Robustness analysis is very important in biology and neuroscience, to unravel behavioural patterns of systems that are conserved despite large parametric uncertainties. To make studies of probabilistic robustness more efficient and scalable when addressing complex models in neuroscience, we propose an alternative to computationally expensive Monte Carlo (MC) methods by introducing and analysing the generalised polynomial chaos (gPC) framework for uncertainty quantification. We consider both intrusive and non-intrusive gPC approaches, which turn out to be scalable and allow for a fast comprehensive exploration of parameter spaces. Focusing on widely used models of neural dynamics as case studies, we explore the trade-off between efficiency and accuracy of gPC methods, and we adopt the proposed methodology to investigate parametric uncertainties in models that feature multiple dynamic regimes.
Code (0)
등록된 구현이 없습니다.
Tasks
Efficient ExplorationUncertainty QuantificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Robustness quantification: a new method for assessing the reliability of the predictions of a classifier
Based on existing ideas in the field of imprecise probabilities, we present a new approach for assessing the reliability of the individual predictions of a generative probabilistic classifier. We call this approach robus…
Uncertainty QuantificationActive Inference Meeting Energy-Efficient Control of Parallel and Identical Machines
We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating…
Decision MakingDeep LearningUncertainty QuantificationFrom Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making proc…
Decision MakingEnsemble LearningUncertainty QuantificationStatistical Guarantees for the Robustness of Bayesian Neural Networks
We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs bet…
General Classificationimage-classificationImage ClassificationRobustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection
Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cop…