The capacity of the Hopfield associative memory
IEEE Transactions on Information Theory
Pattern Recognition
The logic of connectionist systems
Neural computing architectures
VLSI implementation of a high-capacity neural network associative memory
Advances in neural information processing systems 2
Improving sampled probability distributions for Markov random fields
Pattern Recognition Letters
An exponential response neural net
Neural Computation
An analysis of the elastic net approach to the traveling salesman problem
Neural Computation
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recurrent correlation associative memories
IEEE Transactions on Neural Networks
Multivalued associative memories based on recurrent networks
IEEE Transactions on Neural Networks
An analysis of high-capacity discrete exponential BAM
IEEE Transactions on Neural Networks
The decision-making properties of discrete multiple exponential bidirectional associative memories
IEEE Transactions on Neural Networks
Storage Capacity of the Exponential Correlation Associative Memory
Neural Processing Letters
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
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The exponential correlation associative memory (ECAM) is a recurrent neural network model which has large storage capacity and is particularly suited for VLSI hardware implementation. Our aim in this paper is to show how the ECAM model can be entirely derived within a Bayesian framework, thereby providing more insight into the behaviour of this algorithm. The framework for our study is a novel relaxation method which involves direct probabilistic modelling of the pattern corruption mechanism. The parameter of this model is the memoryless probability of error on nodes of the network. This bit-error probability is not only important for the interpretation of the ECAM model, but allows also us to understand some more general properties of Bayesian pattern reconstruction by relaxation. In addition, we demonstrate that both the Hopfield memory and the Boolean network model developed by Aleksander can be regarded as limits of the presented relaxation approach with precise physical meaning in terms of this parameter. To study the dynamical behaviour of our relaxation model, we use the Hamming distance picture of Kanerva which allows us to understand how the bit-error probability evolves during the relaxation process. We also derive a parameter-free expression for the storage capacity of the model which, like a previous result of Chiueh and Goodman, scales exponentially with the number of nodes in the network.