A recurrent neural network, consisting of a small ensemble of eight processing units. In addition to external inputs and outputs, each unit has feedback connections to other units in the network, as indicated by the branches of the output lines that loop back onto the input lines leading into each unit (reproduced b
An RNN can in principle map from the entire history of previous inputs to each output. The idea is t...
Recent advances in machine learning have enabled neural networks to solve tasks humans typically per...
It is an important topic to investigate the mechanisms of sequential patterns of behaviors which are...
Makarov VA, Song Y, Velarde MG, Hübner D, Cruse H. Elements for a general memory structure: properti...
Humans are able to form internal representations of the information they process – a capability wh...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Organization of synaptic connectivity as the basis of neural computation and learning. Single and mu...
The concept of neural network originated from neuroscience, and one of its primitive aims is to help...
The seminar centered around recurrent information processing in neural systems and its connections t...
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has f...
Summary: Recurrent neural networks (RNNs) are designed to learn sequential patterns in silico, but i...
<p>(A) An exemplary recurrent neural network of 12 neurons. The network state has a 4-Winner-Take-A...
This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for...
Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedba...
An RNN can in principle map from the entire history of previous inputs to each output. The idea is t...
Recent advances in machine learning have enabled neural networks to solve tasks humans typically per...
It is an important topic to investigate the mechanisms of sequential patterns of behaviors which are...
Makarov VA, Song Y, Velarde MG, Hübner D, Cruse H. Elements for a general memory structure: properti...
Humans are able to form internal representations of the information they process – a capability wh...
Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of i...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Organization of synaptic connectivity as the basis of neural computation and learning. Single and mu...
The concept of neural network originated from neuroscience, and one of its primitive aims is to help...
The seminar centered around recurrent information processing in neural systems and its connections t...
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has f...
Summary: Recurrent neural networks (RNNs) are designed to learn sequential patterns in silico, but i...
<p>(A) An exemplary recurrent neural network of 12 neurons. The network state has a 4-Winner-Take-A...
This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for...
Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedba...
An RNN can in principle map from the entire history of previous inputs to each output. The idea is t...
Recent advances in machine learning have enabled neural networks to solve tasks humans typically per...
It is an important topic to investigate the mechanisms of sequential patterns of behaviors which are...