Graph neural networks for connectivity inference in spatially patterned neural responses
- Authors
- Park, Taehoon; Kim, JuHyeon; Kang, DongHee; Yoon, Ki jung
- Issue Date
- Dec-2022
- Publisher
- NeurIPS
- Keywords
- continuous attractor networks; connectivity inference; graph neural networks
- Citation
- Conference on Neural Information Processing Systems (Workshop on Symmetry and Geometry in Neural Representations), pp.1 - 12
- Indexed
- OTHER
- Journal Title
- Conference on Neural Information Processing Systems (Workshop on Symmetry and Geometry in Neural Representations)
- Start Page
- 1
- End Page
- 12
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188892
- Abstract
- A continuous attractor network is one of the most common theoretical framework for study- ing a wide range of neural computations in the brain. Many previous approaches have at- tempted to identify continuous attractor systems by investigating the state-space structure of population neural activity. However, establishing the patterns of connectivity for relat- ing the structure of attractor networks to their function is still an open problem. In this work, we propose the use of graph neural networks combined with the structure learning for inferring the recurrent connectivity of a ring attractor network and demonstrate that the developed model greatly improves the quality of circuit inference as well as the prediction of neural responses compared to baseline inference algorithms.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188892)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.