Background: The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. ...
Ron Henkel, Lukas Endler, Andre Peters, Nicolas Le...
In this paper, a general framework for the analysis of a connection between the training of artificial neural networks via the dynamics of Markov chains and the approximation of c...
Understanding self-replication from an information processing perspective is important because, amongother things, it can shed light on molecular mechanismsof biological reproduct...
James A. Reggia, Hui-Hsien Chou, Steven L. Armentr...
This paper presents our implemented computational model for interpreting and generating indirect answers to Yes-No questions. Its main features are 1) a discourse-plan-based appro...
How should we decide among competing explanations of a cognitive process given limited observations? The problem of model selection is at the heart of progress in cognitive scienc...
In Jae Myung, Mark A. Pitt, Shaobo Zhang, Vijay Ba...
Current computational models of bottom-up and top-down components of attention are predictive of eye movements across a range of stimuli and of simple, fixed visual tasks (such a...
Abstract. The aim of this paper is to introduce a novel, biologically inspired approach to extract visual features relevant for controlling and understanding reachto-grasp actions....
A number of computational models of visual attention exist, but making comparisons is difficult due to the incompatible implementations and levels at which the simulations are con...
Albert L. Rothenstein, Andrei Zaharescu, John K. T...
This paper presents a computational model for reasoning with causal explanations of observations within the framework of Abductive Event Calculus (AEC). The model is based on abdu...
We argue that there is currently no satisfactory general framework for comparing the extensional computational power of arbitrary computational models operating over arbitrary doma...