A Spiking Neuron Model of Serial-Order Recall


Vector symbolic architectures (VSAs) have been used to model the human serial-order memory system for decades. Despite their success, however, none of these models have yet been shown to work in a spiking neuron network. In an effort to take the first step, we present a proof-of-concept VSA-based model of serial-order memory implemented in a network of spiking neurons and demonstrate its ability to successfully encode and decode item sequences. This model also provides some insight into the differences between the cognitive processes of memory encoding and subsequent recall, and establish a firm foundation on which more complex VSA-based models of memory can be developed.

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