Abstract
In neural networks, the associative memory is one in which applying some input pattern leads to the response of a corresponding stored pattern. During the learning phase the memory is fed with a number of input vectors and in the recall phase when some known input is presented to it, the network recalls and reproduces the output vector. Here, we improve and increase the storing ability of the memory model proposed in [1]. We show that there are certain instances where their algorithm can not produce the desired performance by retrieving exactly the correct vector. That is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the desired output is just a single vector. Our proposed solution overcomes this and uniquely determines the output vector as some input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of Competitive Cooperative Neurons (CCNs).
Highlights
The ability to store and retrieve information is critical in any type of neural network
The memory, associative memory, can be defined as the one in which the input pattern or vector leads to the response of a corresponding stored pattern
In the case of autoassociative memory, both input and output vectors range over the same vector space
Summary
The ability to store and retrieve information is critical in any type of neural network. The memory, associative memory, can be defined as the one in which the input pattern or vector leads to the response of a corresponding stored pattern (output vector). Given a name (“John”) as input, the system will be able to recall its corresponding phone number (“657-9876”) stored in memory. In the context of neural network, an associative memory consists of neurons (known as conventional McCulloh-Pitts [2] neurons) that are capable of processing input vectors and recalling output vectors. These conventional model neurons use inputs from each source that are characterized by the amplitude of input signals.
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