A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity - Huang, Qiu-Sheng; Wei, Hui (Frontiers in Computational Neuroscience, 2021)
Working memory is closely involved in various cognitive activities, but its neural mechanism is still under exploration. The mainstream view has long been that persistent activity is the neural basis of working memory, but recent experiments have observed that activity-silent memory can also be correctly recalled. The underlying mechanism of activity-silent memory is considered to be an alternative scheme that rejects the theory of persistent activity. We propose a working memory model based on spike-timing-dependent plasticity (STDP). Different from models based on spike-rate coding, our model adopts precise timed patterns of action potentials to represent information, so it can flexibly encode new memory representation. The model can work in both persistent and silent states, i.e., it is compatible with both of these seemingly conflicting neural mechanisms.