This valuable study presents a plastic recurrent spiking network model that spontaneously generates repeating neuronal sequences under unstructured inputs. The authors provide solid evidence that, ...

Abstract: Traditionally, the uncertainty qualification is utilized with the known probability distribution function (PDF). However, in some scenarios, the PDFs of some uncertain variables are modeled ...

Stop letting AI pick your passwords. They follow predictable patterns instead of being truly random, making them easy for ...

Interest Rate Probability Distributions Implied by Derivatives Prices is a daily measure of the distribution of future short-term interest rates, calculated from prices of fixed-income derivatives ...

This investigation was supported (in part) by the U.S. Public Health Service, National Institutes of Health, grant GM-10525. Note: The article usage is presented with a three- to four-day delay and ...

Centrality measures allow to identify important nodes in networked systems. An open question in network theory is the empirical observation that a node’s centrality—whose computation requires ...

A courseware module that covers the fundamental concepts in probability theory and their implications in data science. Topics include probability, random variables, and Bayes' Theorem.

The study of specific physiological processes from the perspective of network physiology has gained recent attention. Modeling the global information integration among the separated functionalized ...

Probability distribution is an essential concept in statistics, helping us understand the likelihood of different outcomes in a random experiment. Whether you’re a student, researcher, or professional ...