Aonan Tang
Affiliations: | Indiana University, Bloomington, Bloomington, IN, United States |
Area:
electrophysiologyGoogle:
"Aonan Tang"Mean distance: 15.23 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| John M. Beggs | grad student | 2012 | Indiana University | |
| (Local cortical networks: Functional topology and dynamics.) | ||||
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Publications
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| Tang A, Beaton EA, Tatham E, et al. (2015) Processing of different types of social threat in shyness: Preliminary findings of distinct functional neural connectivity. Social Neuroscience. 1-23 |
| Chen W, Hobbs JP, Tang A, et al. (2010) A few strong connections: optimizing information retention in neuronal avalanches. Bmc Neuroscience. 11: 3 |
| Yeh FC, Tang A, Hobbs JP, et al. (2010) Maximum entropy approaches to living neural networks Entropy. 12: 89-106 |
| Tang A, Jackson D, Hobbs J, et al. (2008) A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 28: 505-18 |
| Smith JL, Hobbs J, Tang A, et al. (2008) Network Comparison of Epileptogenic Human Cortex and Excited Rat Cortex Journal of Neurosurgery: Pediatrics. 1: A351-A352 |
| Tang A, Honey CJ, Hobbs J, et al. (2008) Information flow in local cortical networks is not democratic Bmc Neuroscience. 9 |
| Hsu D, Tang A, Hsu M, et al. (2007) Simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 76: 041909 |
| Tang A, Hobbs J, Chen W, et al. (2007) A second-order maximum entropy model predicts correlated network states, but not their evolution over time Bmc Neuroscience. 8: P32 |