Bidirectional associative memory

About: Bidirectional associative memory is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 56009 citations. The topic is also known as: DAP & BAM.

Cellular Neural Networks: Theory And Applications

Abstract: CONTENTS: Preface Introduction to Cellular Neural Networks Theory of Cellular Neural Networks: Mathematical Point of View Stability Analysis of Bidirectional Associative Memory CNNs with time delays On the Dynamics of Some Classes of Cellular Neural Networks Spatio-Temporal Phenomena in Two-dimensional Cellular Nonlinear Networks Travelling Waves in FitzHugh-Nagumo Cellular Neural Network Model CNN Applications in Modeling and Solving Non-Electrical Problems CNN for Obstacle Detection in Stereo Vision Imagery Object Tracking and Exact Colour Reproduction for Medical Imaging Criteria for Trained Neural Networks with Appliance in Passive Radiolocation Index.

Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory

Abstract: We study pulse-coupled neural networks that satisfy only two assumptions: each isolated neuron fires periodically, and the neurons are weakly connected. Each such network can be transformed by a piece-wise continuous change of variables into a phase model, whose synchronization behavior and oscillatory associative properties are easier to analyze and understand. Using the phase model, we can predict whether a given pulse-coupled network has oscillatory associative memory, or what minimal adjustments should be made so that it can acquire memory. In the search for such minimal adjustments we obtain a large class of simple pulse-coupled neural networks that ran memorize and reproduce synchronized temporal patterns the same way a Hopfield network does with static patterns. The learning occurs via modification of synaptic weights and/or synaptic transmission delays.

Associative memory in a network of ‘spiking’ neurons

Abstract: The Hopfield network provides a simple model of an associative memory in a neuronal structure. It is, however, based on highly artificial assumptions, especially the use of formal two-state neurons or graded-response neurons. The authors address the question of what happens if formal neurons are replaced by a model of ‘spiking’ neurons. They do so in two steps. First, they show how to include refractoriness and noise into a simple threshold model of neuronal spiking. The spike trains resulting from such a model reproduce the distribution of interspike intervals and gain functions found in real neurons. In a second step they connect the model neurons so as to form a large associative memory system. The spike transmission is described by a synaptic kernel which includes axonal delays, ‘Hebbian’ synaptic efficacies, and a realistic postsynaptic response. The collective behaviour of the system is predicted by a set of dynamical equations which are exact in the limit of a large and fully connected network that...

Associative memories via artificial neural networks

Abstract: Several design techniques that can be used for continuous-time and discrete-time neural networks to realize associative memories are presented. Associative memory is discussed, and neural network models are presented. Some stability concepts are outlined. The applicability of these techniques is demonstrated by means of specific examples that illustrate strengths and weaknesses. >