This new algorithm is used for the synthesis of an associative memory implemented by a recurrent neural network with the connection matrix having upper bounds on the diagonal elements to reduce the total number
The major disadvantage of associative memory is its high cost, thus preventing the large-scale use of associative memory. View chapter Book 2000, Soft Computing and Intelligent SystemsS.K. BASU Review article Pattern segmentation in a binary/analog world: unsupervised learning versus memory storing ...
weight dilutioncapacitybasins of attractionperceptron learningThe consequences of two techniques for symmetrically diluting the weights of the standard Hopfield architecture associative memory model, trained using a non-Hebbian learning rule, are examined. This paper reports e...
Index-based reservoir-computing memory In the “location-addressable” or “parameter-addressable” scenario, the stored memory states within the neural network are activated by a specific location address or an index parameter. The stimulus that triggers the system to switch states can be entirely ...
Although the Kosko subsethood FAM (KS-FAM) can also be classified as a fuzzy morphological associative memory (FMAM), the KS-FAM constitutes a two-layer non-distributive model. In this paper, we prove several theorems concerning the conditions of perfect recall, the absolute storage capacity,...
memory, a type ofcomputer memorythat searches for pattern similarity among its stored content through a process akin to AM. This allows them to retrieve a corresponding address pattern despite being given a noisy one, which is a feature incorporated in most memory models in cognition (Kohonen, ...
The reason is that the hippocampal memory system rapidly learns arbitrary patterns of activity, whereas the neocortical system learns slowly. The slow learning of the neocortex is a requirement for any system that is able to eventually extract and model the similarity structure in its environment. ...
In this paper, we develop a learning algorithm and a synthesis procedure for a class of continuours-time associative neural networks stored m desired patterns, to learn some new memory vectors as well as to remove any undesired spurious states on-line. The method proposed constitutes, in many ...
This modification is to increase the performance of associative memory neural network by avoiding most of the Hopfield neural network limitations. In general, MCA is a single layer neural network uses auto-association tasks and working in two phases, that is learning and convergence phases. MCA ...
A set associative cache memory, comprising: an array of storage elements arranged as M sets by N ways; an allocation unit that allocates the storage elements in response to memory a