Disclosed is a method for long-term, short-term, and hybrid memorization of information using a neural network system. The present invention provides a method and a system for selecting or actively controlling and varying storage efficiency of new information and maintenance efficiency of existing information according to a need. A method for storing information includes the steps of: converting input information into a temporal pattern having a spike shape; and storing the information converted into the temporal pattern in a spiking neural network, wherein, in the storing step, the information is able to be stored by applying a spike-timing-dependent plasticity (STDP) learning rule, which is an unsupervised learning rule, to the spiking neural network.
SPIKING NEURAL NETWORK SYSTEM FOR DYNAMIC CONTROL OF FLEXIBLE STABLE AND HYBRID MEMORY STORAGE