Bidirectional Associative Memory (BAM) is a
supervised learning model in Artificial Neural Network. This is
hetero-associative memory, for an input pattern, it returns another pattern which is potentially of a different size. This phenomenon is very similar to the human brain. Human memory is necessarily associative. It uses a chain of mental associations to recover a lost memory like associations of faces with names, in exam questions with answers, etc.
In such memory associations for one type of object with another, a
Recurrent Neural Network (RNN) is needed to receive a pattern of one set of neurons as an input and generate a related, but different, output pattern of another set of neurons.
Why BAM is required?
The main objective to introduce such a network model is to store hetero-associative pattern pairs.
This is used to retrieve a pattern given a noisy or incomplete pattern.
BAM Architecture:
When BAM accepts an input of
n-dimensional vector
X from set
A then the model recalls
m-dimensional vector
Y from set
B. Similarly when
Y is treated as input, the BAM recalls
X.
Algorithm:
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Storage (Learning): In this learning step of BAM, weight matrix is calculated between M pairs of patterns (fundamental memories) are stored in the synaptic weights of the network following the equation
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Testing: We have to check that the BAM recalls perfectly for corresponding and recalls for corresponding . Using,
All pairs should be recalled accordingly.
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Retrieval: For an unknown vector X (a corrupted or incomplete version of a pattern from set A or B) to the BAM and retrieve a previously stored association:
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Initialize the BAM:
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Calculate the BAM output at iteration :
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Update the input vector :
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Repeat the iteration until convergence, when input and output remain unchanged.
Limitations of BAM:
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Storage capacity of the BAM: In the BAM, stored number of associations should not be exceeded the number of neurons in the smaller layer.
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Incorrect convergence: Always the closest association may not be produced by BAM.