In this paper, we investigate the capabilities of local feedback multilayered networks, a particular class of recurrent networks, in which feedback connections are only allowed from neurons to themselves. In this class, learning can be accomplished by an algorithm that is local in both space and time. We describe the limits and properties of these networks and give some insights on their use for solving practical problems
AbstractBased on a new paradigm of neural networks consisting of neurons with local memory (NNLM), w...
WOS: 000244970900005PubMed ID: 17385626The architecture and training procedure of a novel recurrent ...
The exact form of a gradient-following learning algorithm for completely recurrent networks running ...
In this paper, we investigate the capabilities of local feedback multilayered networks, a particular...
This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasi...
This paper concerns dynamic neural networks for signal processing: architectural issues are consider...
International audienceRecurrent neural networks have been extensively studied in the context of neur...
In this paper, we discuss some properties of Block Feedback Neural Networks (B F N). In the first p...
This paper proposes five partially recurrent neural networks architectures to evaluate the different...
Most known learning algorithms for dynamic neural networks in non-stationary environments need globa...
Many researchers have recently focused their efforts on devising efficient algorithms, mainly based ...
This paper introduces a new class of dynamic multi layer perceptrons, called Block Feedback Neural ...
Gradient descent learning algorithms may get stuck in local minima, thus making the learning subopti...
A fully local algorithm which can automatically detect and learn an unknown pattern is proposed for ...
This paper generalizes the back-propagation method to a general network containing feedback connect...
AbstractBased on a new paradigm of neural networks consisting of neurons with local memory (NNLM), w...
WOS: 000244970900005PubMed ID: 17385626The architecture and training procedure of a novel recurrent ...
The exact form of a gradient-following learning algorithm for completely recurrent networks running ...
In this paper, we investigate the capabilities of local feedback multilayered networks, a particular...
This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasi...
This paper concerns dynamic neural networks for signal processing: architectural issues are consider...
International audienceRecurrent neural networks have been extensively studied in the context of neur...
In this paper, we discuss some properties of Block Feedback Neural Networks (B F N). In the first p...
This paper proposes five partially recurrent neural networks architectures to evaluate the different...
Most known learning algorithms for dynamic neural networks in non-stationary environments need globa...
Many researchers have recently focused their efforts on devising efficient algorithms, mainly based ...
This paper introduces a new class of dynamic multi layer perceptrons, called Block Feedback Neural ...
Gradient descent learning algorithms may get stuck in local minima, thus making the learning subopti...
A fully local algorithm which can automatically detect and learn an unknown pattern is proposed for ...
This paper generalizes the back-propagation method to a general network containing feedback connect...
AbstractBased on a new paradigm of neural networks consisting of neurons with local memory (NNLM), w...
WOS: 000244970900005PubMed ID: 17385626The architecture and training procedure of a novel recurrent ...
The exact form of a gradient-following learning algorithm for completely recurrent networks running ...
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