Optical associative memory with terminal attractor (TA) is proposed for pattern recognition. With numerical simulations, the optimal control parameter in the TA model associative memory is determined. The optimal control parameter is also used in an optical experiment. The capacity of TA model associative memory is also investigated based on the consistency between the stored pattern and the ob...

The present study combines behavioral observations of memory (deferred imitation, DI, after a brief delay of 30 min and after a long delay of 2-3 weeks) and electrophysiological (event-related potentials, ERPs) measures of associative memory, as well as parental reports of non-verbal and verbal communication in sixteen 14-months-old children. Results show that for DI, the children remembered th...

Associative memory in cortical circuits has been held as a major mechanism for content-addressable memory. Hebbian synapses implement associative memory efficiently when storing sparse binary activity patterns. However, in models of sensory processing, representations are graded and not binary. Thus, it has been an unresolved question how sensory computation could exploit cortical associative m...

We apply genetic algorithms to Hop eld's neural network model of associative memory. Previously, using ternary chromosomes, we successfully evolved both random weight matrix and over-loaded Hebbian weight matrix to function as an associative memory. In this paper, we present a real-encoded genetic algorithm to evolve random synaptic weights to store some number of patterns as associative memory...

An essential part of image analysis is the identification of objects within the image. This paper describes the results of applying a binary associative memory neural network (ADAM), to the complex task of identifying shapes in document images. The associative memory is used to implement the generalised Hough Transform, exploiting the fast look-up ability of the associative memory to give a hig...

In this paper, we propose a Kohonen feature map associative memory with area representation for sequential patterns. This model is based on the Kohonen feature map associative memory with area representation and the Kohonen feature map associative memory for temporal sequences. The proposed model can learn sequential patterns successively, and has robustness for damaged neurons. We carried out ...

Associative memory is defined as memory for the relationship between two initially unrelated items, like a name and an unfamiliar face. Associative memory is not only one of the most common forms of memory used in everyday situations, but is highly dependent on the structures of the medial temporal lobe (MTL). The goal of this chapter is to review the patterns of neural activity shown to underl...

A new nonrecurrent associative memory model is proposed. This model is composed of a nonlinear transformation in the spectral domain followed by the association. The Moore-Penrose pseudoinverse is employed to obtain the least squares optimal solution. Computer simulations are done to evaluate the performance of the model. The simulations use one-dimensional speech signals and two-dimensional he...

We present a linguistic extension from a crisp model by using a codification model that allows us to implement a fuzzy system on a discrete decision model. The paper begins with an introduction to the representation of fuzzy information, followed by a discussion of the codification method and the extension of a linear associative memory to a linguistic linear associative memory. Finally, we enu...

Autoassociations of spatio-temporal sequences have been discussed by a number of authors. We propose a mechanism for storing and retrieving pairs of spatio-temporal sequences with the network architecture of the standard bidirectional associative memory (BAM), thereby achieving hetero-associations of spatio-temporal sequences.