A spiking network model for clustering report in a visual working memory task
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
|
| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.1030073/full |
| _version_ | 1797955296638795776 |
|---|---|
| author | Lixing Lei Mengya Zhang Tingyu Li Yelin Dong Yelin Dong Da-Hui Wang Da-Hui Wang Da-Hui Wang |
| author_facet | Lixing Lei Mengya Zhang Tingyu Li Yelin Dong Yelin Dong Da-Hui Wang Da-Hui Wang Da-Hui Wang |
| author_sort | Lixing Lei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T23:31:57Z |
| format | Article |
| id | doaj.art-950429dd83d3495eb818b4becd817c0b |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-10T23:31:57Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-950429dd83d3495eb818b4becd817c0b2023-01-12T06:09:39ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-01-011610.3389/fncom.2022.10300731030073A spiking network model for clustering report in a visual working memory taskLixing Lei0Mengya Zhang1Tingyu Li2Yelin Dong3Yelin Dong4Da-Hui Wang5Da-Hui Wang6Da-Hui Wang7School of Systems Science, Beijing Normal University, Beijing, ChinaSchool of Systems Science, Beijing Normal University, Beijing, ChinaSchool of Systems Science, Beijing Normal University, Beijing, ChinaSchool of Systems Science, Beijing Normal University, Beijing, ChinaDepartment of Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, NY, United StatesSchool of Systems Science, Beijing Normal University, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaBeijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, ChinaIntroductionWorking 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.https://www.frontiersin.org/articles/10.3389/fncom.2022.1030073/fullworking memoryclustering reportheterogeneitySTPspiking network |
| spellingShingle | Lixing Lei Mengya Zhang Tingyu Li Yelin Dong Yelin Dong Da-Hui Wang Da-Hui Wang Da-Hui Wang A spiking network model for clustering report in a visual working memory task Frontiers in Computational Neuroscience working memory clustering report heterogeneity STP spiking network |
| title | A spiking network model for clustering report in a visual working memory task |
| title_full | A spiking network model for clustering report in a visual working memory task |
| title_fullStr | A spiking network model for clustering report in a visual working memory task |
| title_full_unstemmed | A spiking network model for clustering report in a visual working memory task |
| title_short | A spiking network model for clustering report in a visual working memory task |
| title_sort | spiking network model for clustering report in a visual working memory task |
| topic | working memory clustering report heterogeneity STP spiking network |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.1030073/full |
| work_keys_str_mv | AT lixinglei aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT mengyazhang aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT tingyuli aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT yelindong aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT yelindong aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT dahuiwang aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT dahuiwang aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT dahuiwang aspikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT lixinglei spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT mengyazhang spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT tingyuli spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT yelindong spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT yelindong spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT dahuiwang spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT dahuiwang spikingnetworkmodelforclusteringreportinavisualworkingmemorytask AT dahuiwang spikingnetworkmodelforclusteringreportinavisualworkingmemorytask |