A hierarchical organized memory model using spiking neurons

The recent identification of neural cliques, which are network-level memory coding units in the hippocampus, enables population codes to be the neuronal representation of memory. It has been discovered that the timing of spikes plays an important role in the neural computation and information processing in the brain. Moreover, these memory-coding units have been observed organizing in a hierarchical manner in the brain. Inspired by these exciting findings, we present a hierarchically organized memory model with spiking neurons, which can store both associative memory and episodic memory with temporal population codes. The basic structure of the hierarchical model is composed of three layers with different functions and can be extended to more complicated networks by duplicating and connecting the basic three-layer network. With a spike-timing based learning algorithm, the spiking neural network with theta and gamma oscillations is able to store spatiotemporal memory items within gamma cycles, and links these memories into a sequence. The spiking-timing-dependent plasticity (STDP) contributes to the formation of both associative memory and episodic memory via fast and slow N-methyl-D-aspartate (NMDA) channels, respectively.