Redwood Center for Theoretical Neuroscience symposium videos

The Redwood Center for Theoretical Neuroscience at the University of California Berkeley held its inaugural symposium on October 7, 2005. It's now available on video. Buy a DVD for just $5 or watch individual talks online (MPEG or Real Video) free. They include:

• Horace Barlow, Cambridge University: The Roles of Theory, Commonsense, and Guesswork in Neuroscience
  
• Dan Kersten, University of Minnesota: Human Object Perception: Theory, Psychophysics & Imaging
  
• Sue Becker, McMaster University: The role of the hippocampus in memory, contextual gating, stress and depression
  
• Florentin Worgotter, University of Goettingen: Learning in Neurons and Robots
  
• Panel Discussion: The Role and Future Prospects for Math/Computational Theories in Neuroscience
  
• David Heeger, New York University: What fMRI Can Tell Us about How Visual Cortex Works
  
• Kevan Martin, ETH/UNI Zurich: Canonical Circuits for Neocortex
  
• Terry Sejnowski, Salk Institute: Dendritic Darwinism
  
• Jeff Hawkins, Numenta: Prospects and Problems of Cortical Theory

The new centre's mission and research is described as:

Theoretical neuroscience: a sub-discipline within neuroscience which attempts to use mathematical and physical principles to understand the nature of coding, dynamics, circuitry and plasticity in nervous systems.

It is often said that "neuroscience is data-rich yet theory-poor." Our aim is to supply useful algorithms and theoretical ideas to neuroscience in order

• to provide new forms of analysis for neural data (spike trains, EEG, MRI),
  
• to provide theories and specific models which integrate diverse observations and suggest new experimental approaches.

Specific issues and phenomena we are interested in include hierarchical organization and feedback, plasticity, mechanisms of memory, the roles of spike-timing and oscillations, sparse coding, the computation of the thalamo-cortical system and the cortical microcircuit, and the connection between systems-, cellular- and molecular-level neuroscience.

Methodologically, we use ideas from coding theory and probabilistic machine learning insofar as they relate to known neural phenomena and mechanisms in networks, cells and molecules.

I'm sure we'll see some interesting research emerge.