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Spike-timing-dependent plasticity(STDP) is a biological process of synapticmodification caused by the difference of firing order and timing betweenneurons. One of the neurodynamical roles of STDP is to form a macroscopicgeometrical structure in the neuronal state space in response to a periodicinput. This work proposes a practical memory model based on STDP that can storeand retrieve high-dimensional associative data. The model combines STDPdynamics with an encoding scheme for distributed representations and can handlemultiple composite data in a continuous manner. In the auto-associative memorytask where a group of images is continuously streamed to the model, the imagesare successfully retrieved from an oscillating neural state whenever a propercue is given. In the second task that deals with semantic memories embeddedfrom sentences, the results show that words can recall multiple sentencessimultaneously or one exclusively, depending on their grammatical relations.

A STDP-based Encoding Algorithm for Associative and Composite Data