Modelling of neural systems in continuous time

Orthonormal basis functions for modelling continuous-time systems

This paper studies continuous-time system model sets that are spanned by "xed pole orthonormal bases. The nature of these bases is such as to generalise the well-known Laguerre and two-parameter Kautz bases. The contribution of the paper is to establish that the obtained model sets are complete in all of the Hardy spaces H p (P), 1(p(R, and the right half plane algebra A(P) provided that a mild...

Neural Networks and Continuous Time

The fields of neural computation and artificial neural networks have developed much in the last decades. Most of the works in these fields focus on implementing and/or learning discrete functions or behavior. However, technical, physical, and also cognitive processes evolve continuously in time. This cannot be described directly with standard architectures of artificial neural networks such as ...

Continuous-Time Recurrent Neural Networks

CS 296.4 Mathematical Modelling of Continuous Systems

Introduction Robotics and computer vision are interdisciplinary subjects at the intersection of engineering and computer science. By their nature, they deal with both computers and the physical world. Although the former are in the latter, the workings of computers are best described in the black-and-white vocabulary of discrete mathematics, which is foreign to most classical models of reality,...

Anonymity in Continuous-time Systems

Anonymous communication is a rapidly evolving area: it is needed for several privacy related services, such as anonymous electronic voting, payment or consultancy. Numerous techniques have been developed for the main goal: to deliver messages from senders to recipients in a manner that an adversary should find it difficult to identify who communicates with whom. In this paper we analyze two rep...