Add to ORKGThis paper studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net-function and a sigmoid-like continuous activation-function. The authors introduc
We present exact analytical equilibrium solutions for a class of recurrent neural network models, wi...
One way to understand the brain is in terms of the computations it performs that allow an organism t...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...
This paper studies the computational power of various discontinuous real computational models that a...
This article studies the computational power of various discontinuous real computational models that...
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
Recursive neural networks are computational models that can be used to pro- cess structured data. In...
<p>(A) An exemplary recurrent neural network of 12 neurons. The network state has a 4-Winner-Take-A...
AbstractThis paper investigates the dynamics of a class of recurrent neural networks where the neura...
We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and ou...
Recently, fully connected recurrent neural networks have been proven to be computationally rich --- ...
In this paper a neural network for approximating continuous and discontinuous mappings is described....
This dissertation addresses the study of neural networks in which the processing elements are mathem...
We present exact analytical equilibrium solutions for a class of recurrent neural network models, wi...
One way to understand the brain is in terms of the computations it performs that allow an organism t...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...
This paper studies the computational power of various discontinuous real computational models that a...
This article studies the computational power of various discontinuous real computational models that...
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
"Artificial neural networks" provide an appealing model of computation. Such networks consist of an ...
This book presents as its main subject new models in mathematical neuroscience. A wide range of neur...
Recursive neural networks are computational models that can be used to pro- cess structured data. In...
<p>(A) An exemplary recurrent neural network of 12 neurons. The network state has a 4-Winner-Take-A...
AbstractThis paper investigates the dynamics of a class of recurrent neural networks where the neura...
We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and ou...
Recently, fully connected recurrent neural networks have been proven to be computationally rich --- ...
In this paper a neural network for approximating continuous and discontinuous mappings is described....
This dissertation addresses the study of neural networks in which the processing elements are mathem...
We present exact analytical equilibrium solutions for a class of recurrent neural network models, wi...
One way to understand the brain is in terms of the computations it performs that allow an organism t...
AbstractWe investigate the computational power of recurrent neural networks that apply the sigmoid a...