AbstractHava Siegelmann and Eduardo Sontag have shown that recurrent neural networks using the linear-bounded sigmoid are computationally universal. We show that this remains true if the linear-bounded sigmoid is replaced by any function in a fairly large class
We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and ou...
International audienceAnalog and evolving recurrent neural networks are super-Turing powerful. Here,...
AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the typ...
AbstractHava Siegelmann and Eduardo Sontag have shown that recurrent neural networks using the linea...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...
AbstractThis paper shows the existence of a finite neural network, made up of sigmoidal neurons, whi...
AbstractIt has been known for a short time that a class of recurrent neural networks has universal c...
Abstract. This paper shows the existence of a finite neural network, made up of sigmoidal nen-rons, ...
. This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which sim...
We show how to use recursive function theory to prove Turing universality of finite analog recurrent...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
AbstractWe consider analog recurrent neural networks working on infinite input streams, provide a co...
This paper studies the computational power of various discontinuous real computa-tional models that ...
AbstractThis paper deals with finite size networks which consist of interconnections of synchronousl...
This article studies the computational power of various discontinuous real computational models that...
We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and ou...
International audienceAnalog and evolving recurrent neural networks are super-Turing powerful. Here,...
AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the typ...
AbstractHava Siegelmann and Eduardo Sontag have shown that recurrent neural networks using the linea...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...
AbstractThis paper shows the existence of a finite neural network, made up of sigmoidal neurons, whi...
AbstractIt has been known for a short time that a class of recurrent neural networks has universal c...
Abstract. This paper shows the existence of a finite neural network, made up of sigmoidal nen-rons, ...
. This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which sim...
We show how to use recursive function theory to prove Turing universality of finite analog recurrent...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
AbstractWe consider analog recurrent neural networks working on infinite input streams, provide a co...
This paper studies the computational power of various discontinuous real computa-tional models that ...
AbstractThis paper deals with finite size networks which consist of interconnections of synchronousl...
This article studies the computational power of various discontinuous real computational models that...
We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and ou...
International audienceAnalog and evolving recurrent neural networks are super-Turing powerful. Here,...
AbstractWe pursue a particular approach to analog computation, based on dynamical systems of the typ...
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