Machine Perception Laboratory


Infomax Workshop

May 24, 2010

Video recording

The idea of maximizing information gain (Infomax) has appeared with different names in a wide range of fields. In Statistics information gain has been used as a criterion for designing efficient experiments and for improving parameter estimation. In Computational Neuroscience Infomax learning algorithms have proved useful to understand sensory coding in the brain. In Psychophysics information maximization is emerging as a useful framework to understand visual attention and oculomotor behavior. In engineering Infomax approaches are being used to deploy sensor networks that effectively monitor environmental factors. In Robotics information maximization is being used for active exploration and for scheduling behaviors in simple social interactions. In Machine Learning, information gain is being used as a reward mechanism for reinforcement learning algorithms. Recent experiments in neuroscience suggest the existence of neural reward mechanisms related to information gain.

This workshop brings together world experts from apparently disparate fields but whose work uses information gain as a key concept. The goal of the workshop is to find common threads linking these different lines of work so as to help improve our understanding about the role of information on learning and control.

The workshop will be webcasted in real time Monday May 24, 2010, 9 am to 6 pm Pacific Standard Time. To join the workshop go to and enter the webinar ID: 487980072

You will be connected to audio using your computer’s microphone and speakers (VoIP). A headset is recommended. You will also be able to see the powerpoint presentations and ask questions via audio or text.

System Requirements
PC-based attendees
Required: Windows® 7, Vista, XP, 2003 Server or 2000
Macintosh®-based attendees
Required: Mac OS® X 10.4.11 (Tiger®) or newer


  • Tony Bell (UC Berkeley)
  • Ethan Bromberg-Martin (National Eye Institute)
  • Gary Cottrell (UCSD)
  • Bill Geisler ( UT Austin)
  • Andreas Krause (Caltech)
  • Javier R. Movellan (UCSD)
  • Terry Sejnowski (The Salk Institute)
  • Angela Yu (UCSD)

  • Schedule: