一种用于视觉工作记忆任务中聚类报告的脉冲神经网络模型。
A spiking network model for clustering report in a visual working memory task.
作者信息
Lei Lixing, Zhang Mengya, Li Tingyu, Dong Yelin, Wang Da-Hui
机构信息
School of Systems Science, Beijing Normal University, Beijing, China.
Department of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY, United States.
出版信息
Front Comput Neurosci. 2023 Jan 12;16:1030073. doi: 10.3389/fncom.2022.1030073. eCollection 2022.
INTRODUCTION
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.
METHODS
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.
RESULTS
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.
CONCLUSION
Our model provides a new perspective on the phenomenon of visual WM in experiments.
引言
工作记忆（WM）在许多认知过程中起着关键作用，数十年来一直备受关注。最近，人们观察到从均匀分布中采样的记忆颜色报告是聚类的，并且刺激的报告误差遵循高斯分布。
方法
基于成熟的视觉空间工作记忆环形模型，我们构建了一个具有异质连接和嵌入式短期可塑性（STP）的脉冲网络模型，以研究这一有趣现象背后的神经动力学机制。
结果
结果表明，我们的模型再现了从均匀分布中采样的刺激给出的聚类报告以及遵循高斯分布的报告误差。扰动研究表明，连接性和STP的异质性是解释实验观察结果所必需的。
结论
我们的模型为实验中视觉工作记忆现象提供了新的视角。
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