An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memory of neural networks. The long-term memory is represented by ensemble of neural network weights, while the short-term memory is stored as a pool of internal neural network representations of the input pattern. The organization allows the ASNN to incorporate new data cases in short-term memory and provides high generalization ability without the need to retrain the neural network weights. The method can be used to estimate a bias and the applicability domain of models. Applications of the ASNN in QSAR and drug design are exemplified
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
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...
An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural netw...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
This paper will describe a class of networks called Associative Memory Networks which have many desi...
The human brain has a remarkable capability to recall information if a sufficient clue is presented....
This paper presents a deep associative neural network (DANN) based on unsupervised representation le...
The focus of this work are asociative memories as one type of neural networks. We compare models of ...
This article is devoted to the issue of pattern recognition using neural network technologies. In pa...
Artificial neural networks in Neurosciences. This article shows that artificial neural networks are ...
Most of the current neural networks use models which have only tenuous connections to the biological...
The reason for this is easy hardware implementation and successful applications in Associative Memor...
Abstract. Artificial neural networks are brain-like models of parallel computations and cognitive ph...
This paper proposes a novel neural network model for associative memory using dynamical systems. The...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
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...
An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural netw...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
This paper will describe a class of networks called Associative Memory Networks which have many desi...
The human brain has a remarkable capability to recall information if a sufficient clue is presented....
This paper presents a deep associative neural network (DANN) based on unsupervised representation le...
The focus of this work are asociative memories as one type of neural networks. We compare models of ...
This article is devoted to the issue of pattern recognition using neural network technologies. In pa...
Artificial neural networks in Neurosciences. This article shows that artificial neural networks are ...
Most of the current neural networks use models which have only tenuous connections to the biological...
The reason for this is easy hardware implementation and successful applications in Associative Memor...
Abstract. Artificial neural networks are brain-like models of parallel computations and cognitive ph...
This paper proposes a novel neural network model for associative memory using dynamical systems. The...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
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...