Recurrent neural networks have received much attention due to their nonlinear dynamic behavior. One such type of dynamic behavior is that of setting a fixed stable state. This paper shows a counterexample to the claim of A.N. Michel et al. (IEEE Control Systems Magazine, vol. 15, pp. 52-65, Jun. 1995), that sparse constraints on the interconnecting structure for a given neural network are usually expressed as constraints which require that pre-determined elements of T [a real n×n matrix acting on a real n-vector valued function] be zero , for the synthesis of sparsely interconnected recurrent neural networks
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in senso...
Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural a...
Recurrent neural networks are a powerful means to cope with time series. We show how a type of linea...
We study the structure of multistable recurrent neural networks. The activation function is simplifi...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a...
This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical mode...
This letter points out that a statement in the above letter, 1 saying that the sufficiency part of a...
It is known that many discrete-time recurrent neural networks, such as e.g., neural state space mode...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
Steil JJ. Input-Output Stability of Recurrent Neural Networks. Göttingen: Cuvillier; 1999
Recently, fully connected recurrent neural networks have been proven to be computationally rich --- ...
The main result obtained in this paper is that for a neural network with interconnection matrix T, i...
This paper gives a sufficient condition on the weight matrix W of a recurrent neural network of the ...
The comprehension of the mechanisms at the basis of the functioning of complexly interconnected netw...
We establish two conditions which ensure the non-divergence of additive recur-rent networks with uns...
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in senso...
Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural a...
Recurrent neural networks are a powerful means to cope with time series. We show how a type of linea...
We study the structure of multistable recurrent neural networks. The activation function is simplifi...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a...
This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical mode...
This letter points out that a statement in the above letter, 1 saying that the sufficiency part of a...
It is known that many discrete-time recurrent neural networks, such as e.g., neural state space mode...
The Recurrent Neural Networks (RNNs) represent an important class of bio-inspired learning machines ...
Steil JJ. Input-Output Stability of Recurrent Neural Networks. Göttingen: Cuvillier; 1999
Recently, fully connected recurrent neural networks have been proven to be computationally rich --- ...
The main result obtained in this paper is that for a neural network with interconnection matrix T, i...
This paper gives a sufficient condition on the weight matrix W of a recurrent neural network of the ...
The comprehension of the mechanisms at the basis of the functioning of complexly interconnected netw...
We establish two conditions which ensure the non-divergence of additive recur-rent networks with uns...
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in senso...
Recurrent neural networks (RNNs) are widely used throughout neuroscience as models of local neural a...
Recurrent neural networks are a powerful means to cope with time series. We show how a type of linea...