Add to ORKGWe show how to use recursive function theory to prove Turing universality of finite analog recurrent neural nets, with a piecewise linear sigmoid function as activation function. We emphasize the modular construction of nets within nets, a relevant issue from the software engineering point of view
This paper 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...
Understanding the dynamical and computational capabilities of neural models represents an issue of c...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...
. This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which sim...
AbstractHava Siegelmann and Eduardo Sontag have shown that recurrent neural networks using the linea...
Abstract. This paper shows the existence of a finite neural network, made up of sigmoidal nen-rons, ...
AbstractThis paper deals with finite size networks which consist of interconnections of synchronousl...
AbstractThis paper shows the existence of a finite neural network, made up of sigmoidal neurons, whi...
This paper deals with the simulation of Turing machines by neural networks. Such networks are made u...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
In this paper, we provide a historical survey of the most significant results concerning the computa...
We present a complete overview of the computational power of recurrent neural networks involved in a...
International audienceWe consider analog recurrent neural networks working on in nite input streams,...
This article studies the computational power of various discontinuous real computational models that...
This paper 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...
Understanding the dynamical and computational capabilities of neural models represents an issue of c...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...
. This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which sim...
AbstractHava Siegelmann and Eduardo Sontag have shown that recurrent neural networks using the linea...
Abstract. This paper shows the existence of a finite neural network, made up of sigmoidal nen-rons, ...
AbstractThis paper deals with finite size networks which consist of interconnections of synchronousl...
AbstractThis paper shows the existence of a finite neural network, made up of sigmoidal neurons, whi...
This paper deals with the simulation of Turing machines by neural networks. Such networks are made u...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
In this paper, we provide a historical survey of the most significant results concerning the computa...
We present a complete overview of the computational power of recurrent neural networks involved in a...
International audienceWe consider analog recurrent neural networks working on in nite input streams,...
This article studies the computational power of various discontinuous real computational models that...
This paper 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...
Understanding the dynamical and computational capabilities of neural models represents an issue of c...