The segmentation and associative memory using Hindmarsh-Rose neuronal network

We present in this paper some results on the temporal segmentation and retrieval of stored memories or patterns using neural networks composed of the widely used model neurons in the neuroscience society, the bursting Hindmarsh-Rose neurons. For an input pattern which is an overlapped superposition of several stored patterns, it is shown that the proposed neuronal network model is capable of segmenting out each pattern one after another as synchronous firings of a subgroup of neurons, and if a corrupted input pattern is presented, the network is shown to be able to retrieve the perfect one, that is it has the function of associative memory. And we also notice some phenomena in our simulation that still have not been reported elsewhere in our knowledge.