A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining learning and generalisation properties possessed by existing network architectures, allows for arbitrary target patterns for pattern classes with strong convergence properties. The network architecture provides the basis for a pattern recognition system capable of application in a practical environment
A common framework for architectures combining multiple vector-quantization of the input space with ...
A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) devi...
Utilizing the binary RRAM devices, a hardware implemented network based on the modified k-nearest ne...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
This paper presents an overview of novel networking strategies for neural networks which significant...
This paper analyses the parallel implementation using networks of transputers of a neural structure ...
We discuss the adaptable Boolean net neural paradigm together with its learning and generalization p...
Neural networks used as content-addressable memories show unequaled retrieval and speed capabilities...
Based on the assumption that there exists a neu-ral network that efficiently represents a set of Boo...
The most commonly used neural network models are not well suited to direct digital implementations b...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...
In this paper a Boolean neural networks which is able to learn and to control temporal sequences of ...
This work is based on a logical neuron model without weights, the Random Access Memory [1]. For the ...
Abstract. Random Access Memory (RAM) nodes can play the role of artificial neurons that are addresse...
This paper presents a new neural network approach to real-time pattern recognition on a given set of...
A common framework for architectures combining multiple vector-quantization of the input space with ...
A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) devi...
Utilizing the binary RRAM devices, a hardware implemented network based on the modified k-nearest ne...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
This paper presents an overview of novel networking strategies for neural networks which significant...
This paper analyses the parallel implementation using networks of transputers of a neural structure ...
We discuss the adaptable Boolean net neural paradigm together with its learning and generalization p...
Neural networks used as content-addressable memories show unequaled retrieval and speed capabilities...
Based on the assumption that there exists a neu-ral network that efficiently represents a set of Boo...
The most commonly used neural network models are not well suited to direct digital implementations b...
A multi-layered neural assembly is developed which has the capability of learning arbitrary Boolean ...
In this paper a Boolean neural networks which is able to learn and to control temporal sequences of ...
This work is based on a logical neuron model without weights, the Random Access Memory [1]. For the ...
Abstract. Random Access Memory (RAM) nodes can play the role of artificial neurons that are addresse...
This paper presents a new neural network approach to real-time pattern recognition on a given set of...
A common framework for architectures combining multiple vector-quantization of the input space with ...
A novel neuron circuit using a Cu/Ti/Al2O3-based conductive-bridge random access memory (CBRAM) devi...
Utilizing the binary RRAM devices, a hardware implemented network based on the modified k-nearest ne...