Unraveling the Neurodynamic Mechanisms of Visual Working Memory Clustering

Published on January 12, 2023

Imagine your brain as a bustling city, where information is stored and retrieved in a complex web of connections. Scientists have long been fascinated by the role of working memory (WM) in our cognitive processes, and recent research has uncovered an intriguing phenomenon. It turns out that when we remember colors from a random selection, our reports tend to cluster together, as if grouping similar hues into neighborhoods. To shed light on this phenomenon, researchers developed a spiking network model inspired by the way our brains process visual information. This model included various types of connections and short-term plasticity, mimicking the activity of neurons in our brains. By simulating this model, the scientists were able to reproduce the clustering effect observed in real-world experiments. Perturbation studies further revealed that both the diversity of connections and short-term plasticity are crucial for explaining these experimental findings. This groundbreaking research offers a fresh perspective on how visual working memory operates, uncovering the intricate neurodynamic mechanisms at play. To learn more about this fascinating study, check out the full article!

IntroductionWorking 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.MethodsBased 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.ResultsAs 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.ConclusionOur model provides a new perspective on the phenomenon of visual WM in experiments.

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