Ed Connor,PHD

Professor of Neuroscience; Director, Krieger Mind/Brain Institute

Specialization: How the ventral visual pathway represents the world


Johns Hopkins University School of Medicine

3400 N. Charles Street

Krieger Hall 371

Baltimore, MD 21218



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Our studies of higher-level visual cortex are informed by computational, engineering-based approaches.  We investigate neural representation of objects and scenes in the ventral pathway of human/primate visual cortex.  This field has long been hampered by the inability to parameterize shape and quantify neural responses in terms of information filtering. 

Our laboratory has invented experimental and analytical tools to address these obstacles and decipher ventral pathway information processing at a quantitative, neural coding level.  We use shape morphing techniques and genetic algorithms to guide stimulus sampling in the virtual infinity of shape space based on neural response levels.  We are currently experimenting with genetic algorithms guided by large populations of neurons sampled over long time periods with chronic microwire array recording. 

To analyze ventral pathway responses, we parameterize object shapes as clouds of dense samples in multi-dimensional contour, surface, and/or medial axis geometric space.  These parameterizations are used to model neural responses as linear/nonlinear functions based on multi-dimensional Gaussian tuning regions. We are beginning to study how the ventral pathway represents physical properties (e.g. distribution of mass, flexibility, articulation) as well as shape.  In collaboration with Dr. Kristina Nielsen, we are beginning to use 2-photon imaging and array recording to study how local circuits transform local shape information into larger constructs.  Our laboratory is dedicated to developing new experimental/computational methods for collecting and decoding large-scale ventral pathway data and its meaning in relationship to the real world.


To advance neuroscience discovery by uniting neuroscience, engineering and computational data science to understand the structure and function of the brain.