IJCNN 2013 Tutorial (T4) on Brain Connectivity Mapping

• Organizer: Yoonsuck Choe, Ph.D.
  Department of Computer Science and Engineering
  Texas A&M University

  Bio: Yoonsuck Choe is an associate professor and director of the Brain Networks Laboratory in the Department of Computer Science and Engineering at Texas A&M University. His research interests are broadly in computational neuroscience, computational neuroanatomy, neuroinformatics, and neuroevolution. His work ranges from visual cortical modeling (http://computationalmaps.org), sensorimotor learning, temporal aspects of brain function (delay, memory, and prediction), whole brain physical sectioning imaging (Knife-Edge Scanning Microscopy), and web-based brain atlas frameworks (http://kesm.org).

• Abstract: Recent advances in high-throughput, high-resolution 3D microscopy technology are enabling the acquisition of detailed neural connectivity data of whole animal brains. Connectivity data from such instruments are expected to lead to major discoveries regarding brain organization and function. In this tutorial, the latest imaging methods for brain connectivity mapping will be surveyed, along with the algorithms for geometric and topological analysis. How these neural connectivity data can be used in neural networks research will also be discussed.

• Context: This tutorial is timely, in view of the recent announcement by the US government to invest over hundreds of million $ for a comprehensive map of brain activity (The BRAIN Initative). This ambitious government initiative is expected to provide support to a broad range of researchers in neuroscience and neural networks, and familiarity with cutting-edge brain connectivity mapping technology will allow the attendees of this tutorial to be better prepared for the upcoming opportunities and increase the chance of contributing to this massive effort.

• Topics to be covered:
  1. Background on brain connectivity
  2. Imaging methods for brain connectivity mapping
     • Physical sectioning microscopy
     • bright-field and fluorescence microscopy
     • electron microscopy
     • Magnetic resonance imaging (diffusion tensor imaging)
  3. Geometric reconstruction algorithms
  4. Connectivity analysis and neuroinformatics
  5. Potential uses in neural networks research

• Presentation Slides: [PDF for viewing]; [PDF for printing] (Username= "ijcnn2013" Password="choe" [without the quotes])

Disclaimer: The opinions expressed in this web page and the presentation slides are that of the organizer, not of the IJCNN conference or IEEE, or any other entity.