Guiding Questions for Paper Presentations 2
Version: 11/08/2012
2012 Fall Course on Action-Perception-Learning Cycles
(a.k.a. Artificial Neural Networks)
Prof. Byoung-Tak Zhang
Dear Presenters, please attempt to address the following questions when you prepare a paper presentation.
Keep in mind that the presentation time is limited, typically less than 1 hour shared by two or three presenters,
and we also need ample time for discussion which is very important. The issues related with the questions can
also be asked in the exams. So you are also helping other students to prepare their exams.
Note: These questions are minimal and incomplete, giving only a rough guideline for your reading and
presentation. It is highly encouraged for you to ask other interesting questions.
[11-1]
Pouget, A., Dayan, P., & Zemel, R., Inference and computation with population codes, Annu. Rev. Neurosci.,
26:381–410, 2003.

Q1: How do populations of neurons encode information about single variables? How this information can be
decoded from the population activity? How do neural populations realize function approximation?
Q2: How population codes support nonlinear computations over the information they represent?
Q3: How may neural populations offer a rich representation of such things as uncertainty in the aspects of the
stimuli they represent?
Q4: How can populations of neurons represent probability distributions? How can they perform Bayesian
probabilistic inference?
Q5: How multiple aspects of the world are represented in single populations? What computational advantages
(or disadvantages) such schemes have?
[11-2]
Knill, D. C. & Pouget, A., The Bayesian brain: the role of uncertainty in neural coding and computation, Trends
in Neurosciences, 27(12):712-719, 2004.
Q1: What is Bayes optimal? Are human observers Bayes optimal? If yes, how? If not, what might be the reasons?
Q2: How do humans integrate auditory and visual cues to compute the direction of a target from conflicting
information? How can you explain this by the Bayesian coding hypothesis?
Q3: How do neurons or populations of neurons represent uncertainty? What is the neural basis of statistical
inferences?
Q4: What are convolution codes? What is a gain encoding scheme? Give examples of their use. How are they
different?
Q5: How do populations of neurons perform nonlinear Bayesian estimation? How they encode probability
distributions? How they decode them to make probabilistic inference?