Ernst Niebur

Ernst Niebur

Professor of Neuroscience

Krieger 335A
410-516-8643
niebur@jhu.edu
Group/Lab Website

Dr. Ernst Niebur is a professor of neuroscience at the Johns Hopkins University School of Medicine, and a professor of pscychological and brain sciences at the Johns Hopkins Krieger School of Arts and Sciences. His research examines neuronal function at the system level focusing particularly on a function known as selective attention, which is the capability of humans and higher animals to scan sensory input for the most important information and to discard other non-essential information.

He received his BSc and MSc from the University of Dortmund in West Germany. He was awarded his PhD at Universite de Lausanne in Switzerland. He also received a certificate in Artificial Intelligence from the Swiss Federal Institute of Technology (EPFL).

Dr. Niebur has authored or co-authored more than 100 peer-reviewed publications and his work has been cited more than 10,000 times.

Computational Neuroscience

In the Computational Neuroscience Laboratory, we construct quantitative models of biological nervous systems which are firmly based on their neurophysiology, neuroanatomy and behavior, and which are developed in close interaction with experimentalists. The main interest is neuronal function as the system level, reflecting the interaction of subsystems to generate useful behavior. Modeling is particularly important for understanding this and other system level functions since it required the interaction of several pathways and neural functions. One of the functions studied is selective attention, that is the capability of higher animals to scan sensory input for the most important information and to discard all other. Models of the neuronal basis of visual selective attention are constructed by simulating them on digital computers and comparing the results with date obtained from the visual and somatosensory systems of primates. We pay particular attention to the study of mechanisms involving implementation of neural mechanisms which make use of the temporal structure of neuronal firing, rather than just the average firing rate.

B.Hu,R.vonderHeydt, and E.Niebur. Aneuralmodelforperceptualorganizationof3Dsurfaces. In IEEE CISS-2015 49th Annual Conference on Information Sciences and Systems, pages 1–6, Baltimore, MD, 2015. IEEE Information Theory Society

Jamal Molin, Ralph Etienne-Cummings, and Ernst Niebur. How is Motion Integrated into a ProtoObject Based Visual Saliency Model? In 49th Annual Conference on Information Sciences and Systems IEEE-CISS-2015. IEEE Press, 2015

Brian Hu, Ralinkae Kane-Jackson, and Ernst Niebur. A proto-object based saliency model in threedimensional space. Vision Research, 119:42–49, 2016. PMID: 26739278

Junsong Wang, Ernst Niebur, Jinyu Hu, and Xiaoli Li. Suppressing epileptic activity in a neural mass model using a closed-loop proportional-integral controller. Scientific reports, 6:27344, 2016

N. Wagatsuma, R. von der Heydt, and E. Niebur. Spike Synchrony Generated by Modulatory Common Input through NMDA-type Synapses. Journal of Neurophysiology, 116(3):1418–1433, 2016 (CP)

Nobuhiko Wagatsuma, Rüdiger von der Heydt, and Ernst Niebur. Modeling Attention-Induced Reduction of Spike Synchrony in the Visual Cortex. In A. Hirose and S. Ozawa, editors, Proceedings of the 23rd International Conference on Neural Information Processing (ICONIP 2016), pages 1–8, Kyoto, Japan, October 2016

Manuel Gomez-Ramirez, Kristjana Hysaj, and Ernst Niebur. Neural mechanisms of selective attention in the somatosensory system. Journal of Neurophysiology, 116(3):1218–1231, 2016

Grant Gillary and Ernst Niebur. The Edge of Stability: Response Times And Delta Oscillations in Balanced Networks. PLoS Comput Biol, 12(9):e1005121, 2016. PMID: 27689361

B Hu, I. Johnson-Bey, M. Sharma, and Niebur E. Head Movements During Visual Exploration of Natural Images in Virtual Reality. In 51st Annual Conference on Information Systems and Sciences IEEE-CISS. IEEE Press, March 2017 (CP)

Daniel R. Mendat, James E. West, Sudarshan Ramenahalli, Ernst Niebur, and Andreas Andreou. Audio-visual beamforming with the eigenmike microphone array an omni-camera and cognitive auditory features. In 51st Annual Conference on Information Systems and Sciences IEEE-CISS. IEEE Press, 2017 (CP)

ChetanSinghThakur,JamalMolin,TaoXiong,JieZhang,ErnstNiebur,andRalphEtienne-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) 

DanielM.Jeck,MichaelQin,HowardEgeth,andErnstNiebur. Attentivepointinginnaturalscenes correlates with other measures of attention. Vision Research, 135:54–64, 2017. PMID: 28427890 119. 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 120.

Grant Gillary, Rudiger vonderHeydt, and ErnstNiebur. Short term depression and transient memory in sensory cortex. Journal of computational neuroscience, 43(3):273–294, 2017. PMID: 29027605

B Hu, I. Johnson-Bey, M. Sharma, and Niebur E. Head movements are correlated with other measures of visual attention at smaller spatial scales. In 52nd Annual Conference on Information Systems and Sciences IEEE-CISS. IEEE Press, March 2018 (CP)
Updated December 30, 2018 Ernst Niebur, Page 8 of 9

Yao Xu, Chun-Hui Zhang, Ernst Niebur, and Jun-Song Wang. Analytically determining frequency andamplitudeofspontaneousalphaoscillationinJansen’sneuralmassmodelusingthedescribing function method. Chinese Physics B, 27(4):048701, 2018

ElenaMancinelli,ErnstNiebur,andRalphEtienne-Cummings. Computationalstereo-visionmodel of proto-object based saliency in three-dimensional space. In BIOCAS 2018 – Biomedical Circuits and Systems Conference, Cleveland OH, October 2018. IEEE (CP)

TakeshiUejima,ErnstNiebur,RüdigervonderHeydt,andRalphEtienne-Cummings. Proto-Object Based Saliency Model with Second-Order Texture Feature. In BIOCAS 2018 – Biomedical Circuits and Systems Conference, Cleveland OH, October 2018. IEEE (CP)

PierreSacré,MatthewS.D.Kerr,SandyaSubramanian,ZacharyFitzgerald,KevinKahn,MatthewA. Johnson,ErnstNiebur,UriT.Eden,JorgeA.Gonzalez-Martínez,JohnT.Gale,andSrideviV.Sarma. Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system. Proc Natl Acad Sci USA, in press

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