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- Stanislaw Jankowski, Andrzej Lozowski, Jacek M. Zurada
- IEEE Trans. Neural Networks
- 1996

A model of a multivalued associative memory is presented. This memory has the form of a fully connected attractor neural network composed of multistate complex-valued neurons. Such a network is able to perform the task of storing and recalling gray-scale images. It is also shown that the complex-valued fully connected neural network may be considered as a… (More)

A method of extracting intuitive knowledge from neural network classifiers is presented in the paper. An algorithm which obtains crisp rules in the form of logical implications which approximately describe the neural network mapping is introduced. The number of extracted rules can be selected using an uncertainty margin parameter as well as by changing the… (More)

- Adam E. Gaweda, Peter B. Aronhime, +11 authors Tomasz G. Smolinski

Data-Driven Rule Extraction Using Adaptive Fuzzy-Neural Models Adam E. Gaweda August 9, 2002 Neural network and fuzzy rule-based approaches to data-driven modeling have recently gained a lot of attention. The property of universal approximation makes it possible to imitate a large class of complex nonlinear systems with a certain degree of accuracy, while… (More)

Symbolic knowledge extraction from mapping/extrapolating neural networks is presented in the paper. An algorithm to obtain crisp rules in the form of logical implications which roughly describe the neural network mapping is introduced. The number of extracted rules can be selected using an uncertainty margin parameter as well as by changing the precision of… (More)

- Mykola Lysetskiy, Andrzej Lozowski, Jacek M. Zurada
- Neural Processing Letters
- 2002

This paper presents a model of a network of integrate-and-fire neurons with time delay weights, capable of invariant spatio-temporal pattern recognition. Spatio-temporal patterns are formed by spikes according to the encoding principle that the phase shifts of the spikes encode the input stimulus intensity which corresponds to the concentration of… (More)

- Stanislaw Jankowski, Andrzej Lozowski, Jacek M. Zurada
- ISCAS
- 1995

- Andrzej Lozowski, Mykola Lysetskiy, Jacek M. Zurada
- IEEE Transactions on Neural Networks
- 2004

The olfactory system is a very efficient biological setup capable of odor information processing with neural signals. The nature of neural signals restricts the information representation to multidimensional temporal sequences of spikes. The information is contained in the interspike intervals within each individual neural signal and interspike intervals… (More)

- Mykola Lysetskiy, Andrzej Lozowski, Jacek M. Zurada
- Biological Cybernetics
- 2002

This paper proposes temporal-to-spatial dynamic mapping inspired by neural dynamics of the olfactory cortex. In our model the temporal structure of olfactory-bulb patterns is mapped to the spatial dynamics of the ensemble of cortical neurons. This mapping is based on the following biological mechanism: while anterior part of piriform cortex can be excited… (More)

A chaotic cnn associative memory that is able to perform complex pattern separation is presented in this paper. The introduced model has the form of a network composed of chaotic oscillators locally coupled by nonlinear conductances. A local pseudoinverse learning rule for binary pattern storage in the cellular memory structure is proposed. The chaotic… (More)

—Most second-order continuous-time signal processing circuits, whether voltage-mode, current-mode, or mixed-mode, should be capable of realizing s greater than 1/2. However, a number of otherwise interesting circuits have been proposed in the literature which do not have this attribute. In this paper, we show a simple procedure for determining whether can… (More)