Architectural Bias in Recurrent Neural Networks: Fractal Analysis

Tiño P, Hammer B. Architectural Bias in Recurrent Neural Networks: Fractal Analysis. Neural Computation. 2003;15(8):1931-1957

A hybrid architecture using cross-correlation and recurrent neural networks for acoustic tracking in robots

Audition is one of our most important modalities and is widely used to communicate and sense the environment around us. We present an auditory robotic system capable of computing the angle of incidence (azimuth) of a sound source on the horizontal plane. The system is based on some principles drawn from the mammalian auditory system and using a recurrent neural network (RNN) is able to dynamically track a sound source as it changes azimuthally within the environment. The RNN is used to enable fast tracking responses to the overall system. The development of a hybrid system incorporating cross-correlation and recurrent neural networks is shown to be an effective mechanism for the control of a robot tracking sound sources azimuthally

Finite-State Computation in Analog Neural Networks: Steps Towards Biologically Plausible Models?

Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, and constitute the best characterized computational model. On the other hand, neural networks ---proposed almost sixty years ago by McCulloch and Pitts as a simplified model of nervous activity in living beings--- have evolved into a great variety of so-called artificial neural networks. Artificial neural networks have become a very successful tool for modelling and problem solving because of their built-in learning capability, but most of the progress in this field has occurred with models that are very removed from the behaviour of real, i.e., biological neural networks. This paper surveys the work that has established a connection between finite-state machines and (mainly discrete-time recurrent) neural networks, and suggests possible ways to construct finite-state models in biologically plausible neural networks