A neural network model of associative memory is presented which unifies the two historically more relevant enhancements to the basic Little-Hopfield discrete model: the graded resp...
Enrique Carlos Segura Meccia, Roberto P. J. Perazz...
Abstract. A real-time, large scale, leaky-integrate-and-fire neural network processor realized using FPGA is presented. This has been designed, as part of a collaborative project,...
Martin J. Pearson, Ian Gilhespy, Kevin N. Gurney, ...
Traditional collision intensive multi-body simulations are difficult to control due to extreme sensitivity to initial conditions or model parameters. Furthermore, there may be mu...
Abstract. Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) are local in space and time and closely related to a biological model of memory in the prefrontal cortex. N...
This paper describes a default-logic framework (plausibility schemas) and software tools (Decision ApprenticeTM and Legal ApprenticeTM ) for modeling, guiding and automating the r...