Autoassociative memory using refractory period of neurons and its on-line learning

Proposes a novel autoassociative memory model of the neural network consisting of neurons which enter refractory period according to a threshold. We, furthermore, propose the refractory threshold made to change adaptively and autonomously based on network activity. The optimal network activity is then obtained by experiments on a static association model and the value is used to control the threshold. Finally, using network activity, a network with online learning mechanism is also proposed and it is shown that the network can detect unknown patterns and memorise them