We propose a new biological framework, spatial networks of hybrid input/output automata (SNHIOA), for the efficient modeling and simulation of excitable-cell tissue. Within this f...
Ezio Bartocci, Flavio Corradini, Maria Rita Di Ber...
Deep-layer machine learning architectures continue to emerge as a promising biologically-inspired framework for achieving scalable perception in artificial agents. State inference ...
Scripting languages such as R and Matlab are widely used in scientific data processing. As the data volume and the complexity of analysis tasks both grow, sequential data process...
Jiangtian Li, Xiaosong Ma, Srikanth B. Yoginath, G...
The paper presents a framework for semi-supervised nonlinear embedding methods useful for exploratory analysis and visualization of spatio-temporal network data. The method provid...
In the present paper, we present the theoretical basis, as well as an empirical validation, of a protocol designed to obtain effective VC dimension estimations in the case of a si...