An improved architecture for cooperative and comparative neurons (CCNs) in neural network

The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one in which the input pattern leads to the response of a stored pattern (output vector) that corresponds to the input vector. During the learning phase the memory is fed with a number of input vectors that it learns and remembers and in the recall phase when some known input is presented to it, the network exactly recalls and reproduces the required output vector. In this paper, we improve and increase the storing ability of the memory model proposed in [1]. Besides, we show that there are certain instances where the algorithm in [1] does not produce the desired performance by retrieving exactly the correct vector from the memory. 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 correct vector. We propose a simple solution that overcomes this and can uniquely and correctly determine the output vector stored in the associative memory when an input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of memory element called Competitive Cooperative Neuron (CCN).