A spiking network model for clustering report in a visual working memory task

Working memory (WM) plays a key role in many cognitive processes, and great interest has been attracted by WM for many decades. Recently, it has been observed that the reports of the memorized color sampled from a uniform distribution are clustered, and the report error for the stimulus follows a Gaussian distribution.

Based on the well-established ring model for visuospatial WM, we constructed a spiking network model with heterogeneous connectivity and embedded short-term plasticity (STP) to investigate the neurodynamic mechanisms behind this interesting phenomenon.

As a result, our model reproduced the clustering report given stimuli sampled from a uniform distribution and the error of the report following a Gaussian distribution. Perturbation studies showed that the heterogeneity of connectivity and STP are necessary to explain experimental observations.

Our model provides a new perspective on the phenomenon of visual WM in experiments.