Johns Hopkins School of Medicine

Brian Hu, Rüdiger von der Heydt, and Ernst Niebur. Figure-ground organization in natural scenes: Performance of a recurrent neural model compared with neurons of area V2. eNeuro, in press

Pierre Sacré, Matthew S. D. Kerr, Sandya Subramanian, Zachary Fitzgerald, Kevin Kahn, Matthew A. Johnson, Ernst Niebur, Uri T. Eden, Jorge A. Gonzalez-Martinez, John T. Gale, and Sridevi V. Sarma. Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system. Proc Natl Acad Sci USA, 116(4):1404–1413, 2019

Daniel M Jeck, Michael Qin, Howard Egeth, and Ernst Niebur. Unique objects attract attention even when faint. Vision research, 160:60–71, 2019

Yao Xu, Chun-Hui Zhang, Ernst Niebur, and Jun-Song Wang. Analytically determining frequency and amplitude of spontaneous alpha oscillation in Jansen’s neural mass model using the describing function method. Chinese Physics B, 27(4):048701, 2018

Daniel M. Jeck, Michael Qin, Howard Egeth, and Ernst Niebur. Attentive pointing in natural scenes correlates with other measures of attention. Vision Research, 135:54–64, 2017.

Brian Hu and Ernst Niebur. A recurrent neural model for proto-object based contour integration and figure-ground segregation. Journal of computational neuroscience, 43(3):227–242, 2017

Grant Gillary, Rudiger von der Heydt, and Ernst Niebur. Short term depression and transient mem- ory in sensory cortex. Journal of computational neuroscience, 43(3):273–294, 2017.

Chetan Singh Thakur, Jamal Molin, Tao Xiong, Jie Zhang, Ernst Niebur, and Ralph Etienne-Cummings. Neuromorphic visual saliency implementation using stochastic computation. In IEEE International Symposium on Circuits and Systems (ISCAS 2017), Baltimore, MD USA, May 2017. Elsevier (CP)

Gillary, G. and Niebur, E.. (2016) The Edge of Stability: Response Times And Delta Oscillations in Balanced Networks. PLoS Comput Biol, 12(9):e1005121. [pdf]

Dong, Y., Mihalas, S., & Niebur, E. (2011). Improved integral equation solution for the first passage time of leaky integrate-and-fire neurons. Neural Computation, 23 (2), 421–34. [pdf] 

Millman, D., Mihalas, S., Kirkwood, A., & Niebur, E. (2010). Self-organized criticality occurs in non-conservative neuronal networks during Up states. Nature Physics, 6 (10), 801–805. [pdf] 

van Schaik, A., Jin, C., McEwan, A., Hamilton, T., Mihalas, S., & Niebur, E. (2010). A log-domain implementation of the Mihalas-Niebur neuron model. In Circuits and Systems (ISCAS), Proceedings of the 2010 IEEE International Symposium on, pp. 4249–4252. IEEE. 

Russell, A., Orchard, G., Dong, Y., Mihalas, S., Niebur, E., Tapson, J., & Etienne-Cummings, R. (2010). Optimization methods for spiking neurons and networks. IEEE Transactions on Neural Networks, 21 (12), 1950–62. [pdf]

Niebur E.  (2009)  Temporal tagging of attended objects.  Proc Natl Acad Sci U S A. 2009 Feb 24;106(8):2479-80. [LINK]

Masciocchi, C., Mihalas, S., Parkhurst, D., & Niebur, E. (2009). Everyone knows what is interesting: Salient locations which should be fixated. Journal of Vision, 9 (11), 1–22. [pdf]

Mihalas, S. & Niebur, E. (2009). A generalized linear integrate-and-fire neural model produces diverse spiking behavior. Neural Computation, 21 (3), 704–18. [pdf]

Deppmann, C. D., Mihalas, S., Sharma, N., Lonze, B. E., Niebur, E., & Ginty, D. D. (2008). A model for neuronal competition during development.  Science, 320 (5874), 369–73. [pdf]

Dong, Y., Mihalas, S., Qiu, F., von der Heydt, R., & Niebur, E.  (2008). Synchrony  and the binding problem in macaque visual cortex. Journal of Vision, 8 (7), 1–16. [pdf]

Mikula, S. & Niebur, E. (2008). Exact solutions for rate and synchrony in recurrent networks of coincidence detectors. Neural Computation, 20 (11), 2637–61. [pdf]

Ray, S., Hsiao, S. S., Crone, N. E., Franaszczuk, P. J., & Niebur, E. (2008a).  Effect of stimulus intensity on the spike-local field potential relationship in the secondary somatosensory cortex. J. Neurosci., 28 (29), 7334–43. [pdf]