This paper proposes a neural network classifier which can automatically detect the occluded regions in the given image and replace that regions with the estimated values. An auto-associative memory is used to detect outliers such as pixels in the occluded regions. Certainties of each pixels are estimated by comparing the input pixels with the outputs of the auto-associative memory. The input va...

In this article we present the so-called continuous classifying associative memory, able to store continuous patterns avoiding the problems of spurious states and data dependency. This is a memory model based on our previously developed classifying associative memory, which enables continuous patterns to be stored and recovered. We will also show that the behavior of this continuous classifying...

The paper treats the issue of pattern recognition training in terms of Hopfield associative memory (HAM). The conventional randomization technique is used to determine the exponential extremity of HAM recognition error. The extremity exponent is considered as a function of the training process. In training, the exponent is shown to rise from (1-2p) to (1-2p) where p is the error coefficient at ...

We propose learning rules for recurrent neural networks with high-order interactions between some or all neurons. The designed networks exhibit the desired associative memory function: perfect storage and retrieval of pieces of information and/or sequences of information of any complexity.

This paper discusses a model of quantum associative memory which generalizes the completing associative memory proposed by Ventura and Martinez. Similar to this model, our system is based on Grover’s well known algorithm for searching an unsorted quantum database. However, the model presented in this paper suggests the use of a distributed query of general form. It is demonstrated that spurious...

Much literature on group brainstorming has found it to be less effective than individual brainstorming. However, a cognitive perspective suggests that group brainstorming could be an effective technique for generating creative ideas. Computer simulations of an associative memory model of idea generation in groups suggest that groups have the potential to generate ideas that individuals brainsto...

A method of determining the number of responders to a search in an associative memory is presented. It is shown that less than one full adder per memory cell is required and that the maximum delay in establishing the count is proportional to n, where n is the log to the base 3 of the number of memory cells.

The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one in which the input pattern leads to the response of a stored pattern (output vector) that corresponds to the input vector. During the learning phase the memory is fed with a number of input vectors that it learns and re...

The theoretical, practical and technical development of neural associative memories during the last 40 years is described. The importance of sparse coding of associative memory patterns is pointed out. The use of associative memory networks for large scale brain modeling is also mentioned.

A wealth of research has established that retrieval practice promotes subsequent memory, particularly when the retrieval attempt is successful. Furthermore, the number of successful retrievals during practice (i.e., criterion level) dramatically influences final test performance. For example, Vaughn and Rawson (2011) had participants learn Lithuanian– English word pairs via test–restudy practic...