… neural networks [125], this paper is mainly concerned with the real-valued continuous-time
recurrent neural networks described by ordinary differential equations in the time domain. …

… Abstract--In this paper, we prove that any finite time trajectory … of a continuous time recurrent
neural network with n output … of the continuous time recurrent neural network with m units, …

… This paper addresses classification of these signals using neural networks. Neural
networks are used, because they provide a well-established framework for pattern-recognition …

… a linear metric of time. We examine an alternate model in which cortical networks are inherently
able to tell time as a result of time-dependent changes in network state. Using computer …

… In this paper we present a Time-Delay Neural Network (TDNN) … 2) The time-delay arrangement
enables the network to … them independent of position in time and hence not blurred by …

… Dynamical neural networks are … -time recurrent neural networks. Specifically, a fairly
complete description of the possible dynamical behavior and bifurcations of one- and two-neuron …

… Several neural network models in continuous time are reconsidered in the framework of
a general mean-field theory which is exact in the limit of a large and fully connected network. …

… is higher than feedforward networks due to the sequential nature of the … a time delay
neural network architecture which models long term temporal dependencies with training times …

… system based on neural networks for the classification of electrical disturbances in real time.
In … ) thanks in part to the use of this generator as a training tool for the neural networks. The …

… are extracted from recurrent neural networks. Discrete-time recurrent neural networks can
be trained … Rules defining the learned grammar can be extracted from networks in the form of …