Course Identification

Perception in brains and machines

Lecturers and Teaching Assistants

Prof. Ehud Ahissar
Dr. Liron Zipora Gruber, Dr. Guy Nelinger

Course Schedule and Location

First Semester
Tuesday, 10:15 - 12:00, FGS, Rm A

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 2.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 2.00 points


* On 14-Nov-2019, 14:15-16:00 ,a make-up session will be held at FGS Room A.
* Final projects presentation will be held on March 12th, 10:00-15:00 at FGS Room A


Specific knowledge required for the course (can be completed from textbooks):

  • Organization and activity of neurons in sensory systems (primarily vision and touch)
    • Receptive fields, action potentials, neuronal conductance, synaptic interactions
    • Neural responses to simple stimulations

The knowledge will not be crucial for the first two lectures

please contact Ehud if in any doubt (



Language of Instruction


Attendance and participation

Required in at least 80% of the lectures

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)


Evaluation Type

Final assignment

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



Topics that will be addressed include (probably in this order):

  • The phenomenology of perception
  • Philosophical view points along history
  • The theory-experiment and theory-implementation loops
  • Functions of perception: biological vs engineering approaches
  • Basic similarities and differences between brains and computers
  • The acquisition-report dualism
  • Acquisition (principles, embodiment in brains and machines)
  • Current theories of perceptual acquisition in brains and machines
  • Tests (biology) and implementations (engineering) of specific theories
  • Report (principles, embodiment in brains and machines)
  • Current theories of perceptual reports
  • Acquisition-report interactions
  • What does “to perceive” mean for brains and for machines

Learning Outcomes

Better understanding of perception in biological brains and in machines

Reading List

Specific papers, primarily from the list below, will be indicated for reading during the course.

Note the page numbers in bold – these are the pages you are required to read. When no page numbers are mentioned, read the entire article.

PDFs are available at the course site

  1. Von Uexküll, J. (1992/1934) A stroll through the worlds of animals and men: A picture book of invisible worlds. Semiotica 89:319-391 (p. 319-352, 383-390).
  2. Kuhn, T.S. The Structure of Scientific Revolutions (The University of Chicago Press, Chicago and London, 1962) (p. 111 – 135; Chapter X.  Revolutions as Changes of World View).
  3. Chalmers, D.J. What is a neural correlate of consciousness. Neural correlates of consciousness: Empirical and conceptual questions, 17-40 (2000) (p. 1-9 of the pdf file).
  4. Sheinberg, D.L. and N.K. Logothetis (1997) The role of temporal cortical areas in perceptual organization. Proceedings of the National Academy of Sciences 94:3408-3413.
  5. Ahissar, E. & Knutsen, P.M. Vibrissal location coding. Scholarpedia 6, 6639 (2011).
  6. Ahissar, E., G. Nelinger, and L.Z. Gruber (2019) Schematic framework for theories of perception. Scholarpedia 14:52463.
  7. Powers, W.T. (1973) Feedback: beyond behaviorism. Science 179:351-6.
  8. Van Gelder, T. and R.F. Port (1995) It’s about time: An overview of the dynamical approach to cognition. Mind as motion: Explorations in the dynamics of cognition 1:43. (p. 1-15, 36-39)
  9. Poggio, T. and T. Serre (2013) Models of visual cortex. Scholarpedia 8:3516.
  10. O'Regan, J.K. and A. Noe (2001) A sensorimotor account of vision and visual consciousness. Behav Brain Sci 24:939-73; discussion 973-1031 (p. 939-948)
  11. Ahissar, E. and E. Assa (2016) Perception as a closed-loop convergence process. eLife 5:e12830.
  12. Brooks, R. (1986) A robust layered control system for a mobile robot. IEEE journal of robotics and automation 2:14-23.
  13. Premebida, C., R. Ambrus, and Z.-C. Marton (2018) Intelligent Robotic Perception Systems, in Mobile Robots-Volume 1. IntechOpen. p. 111-127.
  14. Oliveira, M., L.S. Lopes, G.H. Lim, S.H. Kasaei, A.M. Tomé, and A. Chauhan (2016) 3D object perception and perceptual learning in the RACE project. Robotics and Autonomous Systems 75:614-626.
  15. Turing, A.M. (1950) Computing machinery and intelligence. Mind 49:433-460.