Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model-Reference-Cited by-同舟云学术

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Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model

Published:2013-02-07 Issue:1 Volume:35 Page:109-124
ISSN:0929-5313
Container-title:Journal of Computational Neuroscience
language:en
Short-container-title:J Comput Neurosci

Author:

Kobayashi Ryota,Kitano Katsunori

Publisher

Springer Science and Business Media LLC

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience,Sensory Systems

Link

http://link.springer.com/content/pdf/10.1007/s10827-013-0443-y.pdf

Reference54 articles.

1. Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics, 16, 412–424.

2. Becchetti, A., Gullo, F., Bruno, G., Dossi, E., Lecchi, M., & Wanke, E. (2012). Exact distinction of excitatory and inhibitory neurons in neural networks: a study with GFP-GAD67 neurons optically and electrophysiologically recognized on multielectrode arrays. Frontiers in Neural Circuits, 6, 63.

3. Beggs, J. M., & Plenz, D. (2003). Neuronal avalanches in neocortical circuits. Journal of Neuroscience, 23, 11167–11177.

4. Brette, R., & Gerstner, W. (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of Neurophysiology, 94, 3637–3642.

5. Cocco, S., Leibler, S., & Monasson, R. (2009). Neuronal coupling between retinal ganglion cells inferred by efficient inverse statistical physics methods. Proceedings of the National Academy of Sciences of the United States of America, 106, 14058–14062.

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