We discuss advantages and disadvantages of temporally continuous neural networks in contrast to clocked ones, and continue with some “tricks of the trade” of continuous time and recurrent neural networks
A continuous-time, continuous-state version of the temporal differ-ence (TD) algorithm is derived in...
Abstract. We have investigated two specific network types in the class of dynamic neural networks: L...
Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedba...
We discuss advantages and disadvantages of temporally continuous neural networks in contrast to cloc...
One of the central questions in neuroscience is how neurons and neuron populations communicate with ...
AbstractWe discuss models for computation in biological neural systems that are based on the current...
The human brain has the capability to organize the neurons (experience-adapted connections) to perfo...
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has f...
AbstractIn mathematical modeling, very often discrete-time (DT) models are taken from, or can be vie...
In this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...
The field of neural networks is a wide and diverse field which spans a variety of interests, modelli...
Motivated partly by the resurgence of neural computation research, and partly by advances in device ...
Aim at the problems that the inputs and outputs of some practical nonlinear systems are Continuous t...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
We test the viability of having learnable timescales in multi-timescales recurrent neural networks
A continuous-time, continuous-state version of the temporal differ-ence (TD) algorithm is derived in...
Abstract. We have investigated two specific network types in the class of dynamic neural networks: L...
Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedba...
We discuss advantages and disadvantages of temporally continuous neural networks in contrast to cloc...
One of the central questions in neuroscience is how neurons and neuron populations communicate with ...
AbstractWe discuss models for computation in biological neural systems that are based on the current...
The human brain has the capability to organize the neurons (experience-adapted connections) to perfo...
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has f...
AbstractIn mathematical modeling, very often discrete-time (DT) models are taken from, or can be vie...
In this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...
The field of neural networks is a wide and diverse field which spans a variety of interests, modelli...
Motivated partly by the resurgence of neural computation research, and partly by advances in device ...
Aim at the problems that the inputs and outputs of some practical nonlinear systems are Continuous t...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
We test the viability of having learnable timescales in multi-timescales recurrent neural networks
A continuous-time, continuous-state version of the temporal differ-ence (TD) algorithm is derived in...
Abstract. We have investigated two specific network types in the class of dynamic neural networks: L...
Recurrent Neural Networks (RNNs) are connectionist models that operate in discrete time using feedba...
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