Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-basedmethods (EDBMs) have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM) to classify individual spike trains o...

Strategic bidding in joint energy and spinning reserve markets is a challenging task from the viewpoint of generation companies (GenCos). In this paper, the interaction between energy and spinning reserve markets is modeled considering a joint probability density function for the prices of these markets. Considering pay-as-bid pricing mechanism, the bidding problem is formulated and solved as a...

Several Artificial Intelligence schemes for reasoning under uncertainty explore either explicitly or implicitly asymmetries among probabilities of various states of their uncer­ tain domain models. Even though the correct working of these schemes is practically con­ tingent upon the existence of a small number of probable states, no formal justification has been proposed of why this should be t...

Extracting redundancies in the data is the main purpose of unsupervised learning and estimating the covariance using Hebbian learning is a widespread way to achieve this. However, Hebbian learning only leads to the extraction of between-unit covariance. Because most associative memories use distributed representations, it would be more useful to extract the covariance of states. Yet, this opera...