Visual Perception and Learning in the Brain

Course Description: 

This course will be built around the contemporary research of vision. First, it will cover the classical approaches of low and high-level vision, visual learning, the neural implementation of perception and learning in the brain, and computational models.  Next, it will critically evaluate the state-of-the-art and explore alternative approaches to the same issues.  Specifically, it will discuss the probabilistic view on vision, and how it changes the research questions in focus.  We will investigate how statistical learning, rule learning, perception and cue-combination as probabilistic inference can expand the range of interpretable phenomena in vision.  We will also cover the issue of possible neural embodiment of such computations and review evidence that supports such an interpretation.

Learning Outcomes: 

Learning Outcomes

  • Getting acquainted with vision research
  • Understanding the link between perception and learning
  • Exploring the probabilistic interpretation of vision and cognitive functions
  • Tying abstract computational and behavioral results to neural implementation of visual coding
  • Gaining experience in how to read and present various scientific materials

The course is pass/fail based on the following three components.

  • Each student will have to make a number of presentations based on the assigned readings during the semester. Making a presentation involves reading the assigned papers, if necessary reading additional material, preparing a brief summary powerpoint presentation of the topic, and leading the discussion during class.  Performance will be evaluated based on how well the student understood and presented the essence (!) of the topic rather than meandering, how well s/he could keep the presentation conscience and within proper time frame, and how well s/he integrates the given topic with the previous topics discussed in class.  The powerpoint presentations will be collected and used in the final evaluation.
  • In addition, each student needs to read each assigned paper for each class (before the class!).  This does not have to be a deep thorough reading (although that is the best), however it must be sufficient to be familiar with the topic covered in the paper.  Having said that, reading of the course material is essential component of the course, and keeping up with the readings will be expected.  To facilitate this, each student has to submit (before the class by E-learning Dropbox) and also bring to class a copy of a one-page summary sheet.  On this sheet, for each paper four items need to be presented in an itemized manner: a) one-two sentences about the gist of the paper, b) a single idea/result/methodological trick that was the most interesting, c) the list of topics, notions, equations that the student did not understand or did not agree with, d) at least one question that s/he wants to clarify based on the study that defines the next step in the research.  During the class the student will present his/her summary and the question, which will be discussed in class. Students will be required to reach and present an answer to the question by the end of the class. When the student is one of the presenters in the class, s/he is still required to present a summary sheet of the other papers presented that day, but s/he does not need to come up with a presentable question.
  • Participation in class sessions. This is a small, seminar-style course with the goal of integrating several topics.  It is essential that the class formed a coherent view on the covered topics by the end of the semester.  To achieve this, I expect a highly interactive and critical discussing during classes.


Required Materials:

  • PDFs of the reading will be provided.
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