Temporally coherent perturbation of neural dynamics during retention alters human multi-item working memory

• Published in: Progress in neurobiology Vol. 201; p. 102023
• Main Authors: Li, Jiaqi, Huang, Qiaoli, Han, Qiming, Mi, Yuanyuan, Luo, Huan
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2021
• Subjects: Brain; Cognition; Dynamic perturbation; Hidden state; Humans; Memory manipulation; Memory, Short-Term; Neuronal Plasticity; Recency effect; Short-term plasticity (STP); Working memory; Recency effect; Hidden state; Dynamic perturbation; Working memory; Short-term plasticity (STP); Memory manipulation
• ISSN: 0301-0082; 1873-5118; 1873-5118
• DOI: 10.1016/j.pneurobio.2021.102023

•A “dynamic perturbation” approach manipulates multi-item working memory in humans.•Task-irrelevant flickering color probes during retention impacts recency effect.•“Synchronization” disrupts recency effect and “Order Reversal” reverses recency effect.•“Dynamic perturbation” modifies synaptic efficacies of memory items through STP. Temporarily storing a list of items in working memory (WM), a fundamental ability in cognition, has been posited to rely on the temporal dynamics of multi-item neural representations during retention. However, the causal evidence, particularly in human subjects, is still lacking, let alone WM manipulation. Here, we develop a novel “dynamic perturbation” approach to manipulate the relative memory strength of WM items held in human brain, by presenting temporally correlated luminance sequences during retention to interfere with the multi-item neural dynamics. Six experiments on more than 150 subjects confirm the effectiveness of this WM manipulation approach. A computational model combining continuous attractor neural network (CANN) and short-term synaptic plasticity (STP) principles further reproduces all the empirical findings. The model reveals that the “dynamic perturbation” modifies the synaptic efficacies of WM items through STP principles, eventually leading to changes in their relative memory strengths. Our results support the causal role of temporal dynamics of neural network in mediating multi-item WM, and offer a promising, purely bottom-up approach to manipulate WM.