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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ohtsu, S. Nomura, T. Uno, S. Maeda, K. Hayashida, Y. Yagi, T. |
| Copyright Year | 2015 |
| Description | Author affiliation: Grad. Sch. of Eng. Sci., Osaka Univ., Toyonaka, Japan (Ohtsu, S.; Nomura, T.) || Grad. Sch. of Eng., Osaka Univ., Suita, Japan (Uno, S.; Maeda, K.; Hayashida, Y.; Yagi, T.) |
| Abstract | Analyzing network architecture and spatio-temporal dynamics of the visual cortical areas can facilitate understanding visual information processing in the brain. Recently, several physiological experiments utilizing the fast in-vivo imaging technique have demonstrated that the primary visual cortex (V1) and the secondary visual cortex (V2) in mice exhibit complex properties of the responses to visual and electrical stimuli. In order to provide a tool for quantitatively analyzing such a complex dynamics of the cortices at the level of neurons and circuits, here, we constructed a physiologically plausible large-scale network model of the layers 2/3 of V1 and V2, composed of 14,056 multi-compartment neuron models. The Message-Passing-Interface-based parallel simulations of our network model were able to reproduce, at least quantitatively, the neural responses experimentally observed in mouse V1 and V2 with the voltage-sensitive dye imaging. |
| Starting Page | 3957 |
| Ending Page | 3960 |
| File Size | 825029 |
| Page Count | 4 |
| File Format | |
| ISSN | 1557170X |
| e-ISBN | 9781424492718 |
| DOI | 10.1109/EMBC.2015.7319260 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-25 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Brain modeling Visualization Mice Firing Computational modeling Physiology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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