Ed Connor PhD

Professor of Neuroscience

Telephone Number: 410-516-7342
Fax Number: 410-516-8648

The Solomon H. Snyder Department of Neuroscience
Johns Hopkins University
3400 N. Charles St., Baltimore, MD 21218
Room: Brain/ Krieger 371
Lab Page
Areas of Research
Systems, Cognitive + Computational Neuroscience
Neural Circuits, Ensembles + Connectomes

Graduate Program Affiliations

Neuroscience Training Program

Psychological and Brain Sciences

Visual Neuroscience Training Program

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    Neural population representation of a heart-shaped stimulus, derived from response rates of curvature/position-tuned cells in area V4. The left-hand horizontal axis represents boundary curvature, running from -0.3 (concave) through 0.0 (straight) to 1.0 (sharp convex). The right-hand horizontal axis represents object-centered position in degrees, with 0° corresponding to "right", 90° to "top", 180° to "left", and 270° to "bottom" relative to center of mass. The vertical axis (as well as color) represents response strength, derived by summing tuning functions across the cell population, with each cell's tuning function weighted by the cell's response to the heart-shaped stimulus. The surface contains two peaks near curvature 0.7 (broad convex) at positions 45° (upper right) and 135° (upper left), one peak at curvature 1.0 (sharp convex) and position 270° (bottom), and smaller peaks representing intervening concavities. These of course are the defining boundary features of a classic "heart" shape

Object Synthesis in Higher Level Visual Cortex

Our focus is on shape representation in the ventral, object-processing pathway of primate visual cortex, which includes areas V1 (primary visual cortex), V2, V4, and several further stages in inferior temporal cortex. This pathway functions to represent, interpret, store, and recall information about visual objects, and thus underlies much of our ability to recognize other individuals, interpret their facial expressions, comprehend symbolic written information, and generally to identify and interact appropriately with items in the environment. These impressive feats of visual information processing imply complex neural mechanisms that far surpass any artifical systems yet devised.

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