Maintenance of delay-period activity in working memory task is modulated by local network structure

Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM. The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. To further understand the relevance of structure and function in brain neural networks, we explored how WM activity duration is affected by network structure in a WM task function. Firstly, we constructed a spiking neural network and found a dependence of WM activity duration on synaptic currents. This dependence is consistent with the recent experimental observation of a gradient in receptor expression along the WM gradient network. Second, we performed the WM task independently by generating different randomized networks. It was found that network structure can be a key factor in separating persistent and non-persistent activities during the delay period. Over-expression of structures representing information transmission and cycle contributes to the maintenance of WM activity. Finally, in conjunction with anatomical data, we modeled the duration of WM activity in different brain regions. We suggest that WM-related activity relies on interactions between local and distributed networks.