Add to ORKGA model for a class of high-capacity associative memories is presented. Since they are based on two-layer recurrent neural networks and their operations depend on the correlation measure, these associative memories are called recurrent correlation associative memories (RCAMs). The RCAMs are shown to be asymptotically stable in both synchronous and asynchronous (sequential) update modes as long as their weighting functions are continuous and monotone nondecreasing. In particular, a high-capacity RCAM named the exponential correlation associative memory (ECAM) is proposed. The asymptotic storage capacity of the ECAM scales exponentially with the length of memory patterns, and it meets the ultimate upper bound for the capacity of associative m...
Abstract—We consider the problem of neural association for a network of non-binary neurons. Here, th...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A model for a class of high-capacity associative memories is presented. Since they are based on two-...
In this paper we describe the VLSI design and testing of a high capacity associative memory which w...
In this paper we describe the VLSI design and testing of a high capacity associative memory which we...
In this paper, we analyze the recurrent correlation associative memory (RCAM) model of Chiueh and Go...
A generalized associative memory model with potentially high capacity is presented. A memory of this...
Various algorithms for constructing weight matrices for Hopfield-type associative memories are revie...
In this paper we introduce an improved binary correlation matrix memory (CMM) with better storage ca...
Recurrent networks have been proposed as a model of associative memory. In such models, memory items...
Authors have proposed an asymmetrical associative neural network (NN) using variable hysteresis thre...
Associative networks have long been regarded as a biologically plausible mechanism for memory storag...
Abstruct- Most of the neural network associative memory models deal with the storage of binary vecto...
Rückert U. An Associative Memory with Neural Architecture and its VLSI Implementation. In: Milutinov...
Abstract—We consider the problem of neural association for a network of non-binary neurons. Here, th...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A model for a class of high-capacity associative memories is presented. Since they are based on two-...
In this paper we describe the VLSI design and testing of a high capacity associative memory which w...
In this paper we describe the VLSI design and testing of a high capacity associative memory which we...
In this paper, we analyze the recurrent correlation associative memory (RCAM) model of Chiueh and Go...
A generalized associative memory model with potentially high capacity is presented. A memory of this...
Various algorithms for constructing weight matrices for Hopfield-type associative memories are revie...
In this paper we introduce an improved binary correlation matrix memory (CMM) with better storage ca...
Recurrent networks have been proposed as a model of associative memory. In such models, memory items...
Authors have proposed an asymmetrical associative neural network (NN) using variable hysteresis thre...
Associative networks have long been regarded as a biologically plausible mechanism for memory storag...
Abstruct- Most of the neural network associative memory models deal with the storage of binary vecto...
Rückert U. An Associative Memory with Neural Architecture and its VLSI Implementation. In: Milutinov...
Abstract—We consider the problem of neural association for a network of non-binary neurons. Here, th...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...