Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

In the past half century, we have experienced two major waves of methodological
development in the study of human behavior in space and time. The fIrst wave was the well …

Neural models of associative memories are usually concerned with the storage and the
retrieval of binary or bipolar patterns. Thus far, the emphasis in research on morphological …

Associative neural memories are models of biological phenomena that allow for the storage
of pattern associations and the retrieval of the desired output pattern upon presentation of a …

In recent years, heightened interest has been ignited in associative memory networks,
largely attributed to their perceived equivalence with the attention mechanism, a …

The Hopfield-type networks with asymmetric interconnections are studied from the
standpoint of taking them as computational models. Two fundamental properties, feasibility …

In this paper, we generalize the bipolar recurrent correlation neural networks (RCNNs) of
Chiueh and Goodman for patterns whose components are in the complex unit circle. The …

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an
improved version of the bipolar correlation-based Hopfield neural network, can be used to …

Morphological neural networks (MNNs) are a class of artificial neural networks whose
operations can be expressed in the mathematical theory of minimax algebra. In a …

Morphological associative memories (MAMs) are based on a lattice algebra known as
minimax algebra. In previous papers, we gained valuable insight into the storage and recall …