AbstractTriangular dynamical systems can be used to model neural networks of forward type (FNN). In this paper, we establish some convergence theorem for such systems, which indicate how FNN should be implemented to perform the task for which they are designed
Abstract—Attractor dynamics is a crucial problem for attractor neural networks, as it is the underli...
UNM Technical Report No. EECE93 001This report presents a formalism that enables the dynamics of a b...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
AbstractTriangular dynamical systems can be used to model neural networks of forward type (FNN). In ...
A necessary and sufficient condition for a discrete dynamical system to be globally stable and plus...
AbstractWe study a system of retarded functional differential equations which generalise both the Ho...
We study the probabilistic generative models parameterized by feedforward neural networks. An attrac...
This paper deals with a class of large-scale nonlinear dynamical systems, namely the additive neural...
The paper considers a large class of additive neural networks where the neuron activations are model...
This correspondence proves a convergence result for the Lotka-Volterra dynamical systems with symmet...
This report presents a formalism that enables the dynamics of a broad class of neural networks to be...
In a series of papers published in the seventies, Grossberg has developed a geometric approach for a...
The paper considers a general class of neural networks possessing discontinuous neuron activations a...
In this paper we show how a recurrent neural network, of shunting type, receiving changing input can...
In this paper we show how a recurrent neural network, of shunting type, receiving changing input can...
Abstract—Attractor dynamics is a crucial problem for attractor neural networks, as it is the underli...
UNM Technical Report No. EECE93 001This report presents a formalism that enables the dynamics of a b...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
AbstractTriangular dynamical systems can be used to model neural networks of forward type (FNN). In ...
A necessary and sufficient condition for a discrete dynamical system to be globally stable and plus...
AbstractWe study a system of retarded functional differential equations which generalise both the Ho...
We study the probabilistic generative models parameterized by feedforward neural networks. An attrac...
This paper deals with a class of large-scale nonlinear dynamical systems, namely the additive neural...
The paper considers a large class of additive neural networks where the neuron activations are model...
This correspondence proves a convergence result for the Lotka-Volterra dynamical systems with symmet...
This report presents a formalism that enables the dynamics of a broad class of neural networks to be...
In a series of papers published in the seventies, Grossberg has developed a geometric approach for a...
The paper considers a general class of neural networks possessing discontinuous neuron activations a...
In this paper we show how a recurrent neural network, of shunting type, receiving changing input can...
In this paper we show how a recurrent neural network, of shunting type, receiving changing input can...
Abstract—Attractor dynamics is a crucial problem for attractor neural networks, as it is the underli...
UNM Technical Report No. EECE93 001This report presents a formalism that enables the dynamics of a b...
In this paper we present a class of nonlinear neural network models and an associated learning algor...
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