Abstract
In this paper, we present a functional spiking-neuron model of human working memory (WM). This model combines neural firing for encoding of information with activity-silent maintenance. While it used to be widely assumed that information in WM is maintained through persistent recurrent activity, recent studies have shown that information can be maintained without persistent firing; instead, information can be stored in activity-silent states. A candidate mechanism underlying this type of storage is short-term synaptic plasticity (STSP), by which the strength of connections between neurons rapidly changes to encode new information. To demonstrate that STSP can lead to functional behavior, we integrated STSP by means of calcium-mediated synaptic facilitation in a large-scale spiking-neuron model. The model simulated a recent study that measured behavior and EEG activity of participants in a delayed-response task. In this task, a visual grating had to be maintained in WM, and compared to a subsequent probe. It was demonstrated that WM contents could be decoded from the neural activity elicited by a task-irrelevant stimulus that was displayed during the activity-silent maintenance period. In support of our model, we show that it can perform this task, and that both its behavior as well as its neural representations correspond to the human data. We conclude that information in WM can be effectively maintained in activity-silent states by means of calcium-mediated STSP.
Author Summary Mentally maintaining information for short periods of time in working memory is crucial for human adaptive behavior. It was recently shown that the human brain does not only store information through neural firing – as was widely believed – but also maintains information in activity-silent states. Here, we present a detailed neural model of how this could happen in our brain through short-term synaptic plasticity: rapidly adapting the connection strengths between neurons in response to incoming information. By reactivating the adapted network, the stored information can be read out later. We show that our model can perform a working memory task as accurate as human participants can, while using similar mental representations. We conclude that our model is a plausible and effective neural implementation of human working memory.