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A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory. The Hopfield network, named for John Hopfield , consists of a single layer of neurons, where each neuron is connected to every other neuron except itself.
A tuple space is an implementation of the associative memory paradigm for parallel/distributed computing. It provides a repository of tuples that can be accessed concurrently. As an illustrative example, consider that there are a group of processors that produce pieces of data and a group of processors that use the data.
Content-addressable memory (CAM) is a special type of computer memory used in certain very-high-speed searching applications. It is also known as associative memory or associative storage and compares input search data against a table of stored data, and returns the address of matching data. [1]
Introduced by Bart Kosko, [64] a bidirectional associative memory (BAM) network is a variant of a Hopfield network that stores associative data as a vector. The bidirectionality comes from passing information through a matrix and its transpose. Typically, bipolar encoding is preferred to binary encoding of the associative pairs.
In the original Hopfield model of associative memory, [6] the variables were binary, and the dynamics were described by a one-at-a-time update of the state of the neurons. An energy function quadratic in the V i {\displaystyle V_{i}} was defined, and the dynamics consisted of changing the activity of each single neuron i {\displaystyle i} only ...
Conventionally, fixed points in this model represent encoded memories. These models have been used to explain associative memory, classification, and pattern completion. Hopfield nets contain an underlying energy function [4] that allow the network to asymptotically approach a stationary state. One class of point attractor network is ...
The memory or storage capacity of BAM may be given as (,), where "" is the number of units in the X layer and "" is the number of units in the Y layer. [3]The internal matrix has n x p independent degrees of freedom, where n is the dimension of the first vector (6 in this example) and p is the dimension of the second vector (4).
Autoassociative memory, also known as auto-association memory or an autoassociation network, is any type of memory that is able to retrieve a piece of data from only a tiny sample of itself. They are very effective in de-noising or removing interference from the input and can be used to determine whether the given input is “known” or ...