Ernst Niebur PhD

Professor of Neuroscience
Telephone Number: 410-516-8643
Fax Number: 410-516-8648

Johns Hopkins University
Mind/Brain Institute
3400 N. Charles St.
Baltimore, MD 21218
Room: Brain/ Krieger Hall 335A
Lab Page
Areas of Research
Systems, Cognitive + Computational Neuroscience
Neural Circuits, Ensembles + Connectomes

Graduate Program Affiliations

Neuroengineering Training Initiative

Neuroscience Training Program

Psychological and Brain Sciences

Visual Neuroscience Training Program

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    Structure of a model network for perceptual grouping in the primate visual system. Each circle stands for a population of neurons with similar receptive fields. Edges and other local features of an object (black parallelogram in this example) activate edge-selective cells (E). The network of interacting excitatory (B) and inhibitory (IE and IB) cell populations leads to the activation of grouping (G) cells that represent this object. Executive structures select a visual object by exciting the corresponding G cell population directly (top). A consequence of this circuit structure is that B cells are border ownership selective, i.e. they represent whether an edge in the visual scene is part of a foreground object or of the background. Neurons with this property are found mainly in extrastriate cortex by the group of Prof. von der Heydt.

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.

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