Neural networks consist of computational units (neurons) that are linked by a directed graph with some degree of connectivity (network). The connections comprising the edges in the graph are termed weights. As the name suggests the magnitude of the weight determines the magnitude of the effect that the connecting neuron can have upon its target partner. In caricature, neural networks use the ma...

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on...

The Hopfield recurrent neural network is a classical auto-associative model of memory, in which collections of symmetrically coupled McCulloch-Pitts binary neurons interact to perform emergent computation. Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministi...

| In the present paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron training is evident. The perceptron training in the synthesis algorithms is guaranteed to converge for t...

Auto-associative network models have proven extremely useful in modelling the hypothesised function of the CA3 region of the hippocampus in declarative memory. To date, the majority of these models have made use of Hebbian plasticity rules mediated by correlations between mean firing rates. However, recent neurobiological evidence suggests that synaptic plasticity in the hippocampus, and many o...

In the present paper, an effort has been made for storing and recalling images with Hopfield Neural Network Model of auto-associative memory. Images are stored by calculating a corresponding weight matrix. Thereafter, starting from an arbitrary configuration, the memory will settle on exactly that stored image, which is nearest to the starting configuration in terms of Hamming distance. Thus gi...

In this paper, a new synthesis approach is developed for associative memories based on the perceptron training algorithm. The design (synthesis) problem of feedback neural networks for associative memories is formulated as a set of linear inequalities such that the use of perceptron training is evident. The perceptron training in the synthesis algorithms is guaranteed to converge for the design...