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
We provide an overview of theories of continuous time computation. These theories allow us to unders...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
This thesis proposes a learning approach for continuous-time recurrent neural network (CTRNN) archit...
We discuss advantages and disadvantages of temporally continuous neural networks in contrast to cloc...
AbstractIn mathematical modeling, very often discrete-time (DT) models are taken from, or can be vie...
This paper reviews some basic issues and methods involved in using neural networks to respond in a d...
One of the central questions in neuroscience is how neurons and neuron populations communicate with ...
A continuous-time, continuous-state version of the temporal differ-ence (TD) algorithm is derived in...
In this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...
We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a ...
This review attempts to provide an insightful perspective on the role of time within neural network ...
We propose a learning method that, dynamically modi- fies the time-constants of the continuous-time...
We survey learning algorithms for recurrent neural networks with hidden units, and put the various t...
This is the final version. Available on open access from Springer via the DOI in this recordContinuo...
We test the viability of having learnable timescales in multi-timescales recurrent neural networks
We provide an overview of theories of continuous time computation. These theories allow us to unders...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
This thesis proposes a learning approach for continuous-time recurrent neural network (CTRNN) archit...
We discuss advantages and disadvantages of temporally continuous neural networks in contrast to cloc...
AbstractIn mathematical modeling, very often discrete-time (DT) models are taken from, or can be vie...
This paper reviews some basic issues and methods involved in using neural networks to respond in a d...
One of the central questions in neuroscience is how neurons and neuron populations communicate with ...
A continuous-time, continuous-state version of the temporal differ-ence (TD) algorithm is derived in...
In this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...
We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a ...
This review attempts to provide an insightful perspective on the role of time within neural network ...
We propose a learning method that, dynamically modi- fies the time-constants of the continuous-time...
We survey learning algorithms for recurrent neural networks with hidden units, and put the various t...
This is the final version. Available on open access from Springer via the DOI in this recordContinuo...
We test the viability of having learnable timescales in multi-timescales recurrent neural networks
We provide an overview of theories of continuous time computation. These theories allow us to unders...
Attractor properties of a popular discrete-time neural network model are illustrated through numeric...
This thesis proposes a learning approach for continuous-time recurrent neural network (CTRNN) archit...
DOI
Add to ORKG