Action-Perception-Learning Cycles
  • 2012 Fall Graduate Course in Computer Science and Engineering, Cognitive Science, and Brain Science
  • Instructor: Prof. Byoung-Tak Zhang
  • TA: Ha-Young Jang
  • Classroom: 302-209
  • Time: Tue & Thu, 11:00-12:15
  • Reference:
  • Evaluation:
    • Two open-book exams (70%)
    • Paper presentations (20%)
    • Participation and discussion (10%)
  • Announcement:
  • Course Description:
    • How can the brain learn so fast, flexibly, and robustly? What representational mechanisms and organizational principles does the brain use? How can we apply these principles to constructing intelligent cognitive machines that learn like humans? To address these questions, it is important to observe that the brain is embodied with sensors and actuators, and interacts with its environment in a continuous perception-action cycle. Living in a dynamic environment under uncertainty requires the brain to learn moment by moment in real time and incrementally in this continuous, rapid perception-action cycle. In this course we review recent experimental and theoretical work on perception-action cycles and neural coding principles in the brain. We also study mathematical tools developed in information theory, control theory, and Bayesian statistics that may be useful to model the biological information processing in the brain. The goal is to develop computational models of sequential learning processes, i.e. action-perception-learning cycle machines, that enable rapid, continuous, and reliable action and decision-making in a changing environment over an extended period of time or lifelong.
    • Lecture Schedule
    • Week Topics Slides Papers Presenter

      Week 1

      (9/4, 9/6)
      • From machine learning to brain-like cognitive learning
      • Brain as a physical, thermodynamic computer
      • Perception-action cycles and Carnot cycles
      • Models of action-perception-learning cycles
      		 Action-Perception Learning Cycles    

      Week 2

      (9/11, 9/13)
      • Gordon, G., Kaplan, D. M., Lankow, B., Little, D. Y.-J., Sherwin, J., Suter, B. A., and Thaler, L., Toward an integrated approach to perception and action: conference report and future directions, Frontiers in Systems Neuroscience, 5(20):1-6, 2011.
      • Fuster, J. M., The prefrontal cortex - an update: Time is of the essence, Neuron, 30:319-333, 2001.
        (Ref : Fuster, J. M., The cognit: A network model of cortical representation, International Journal of Psychophysiology, 60:125-132, 2006.)
      		 2-1

      2-2

      		 2-1

      2-2-1
      2-2-2

      		 Beom-Jin Lee


      Hyo-Sun Chun

      Week 3

      (9/18, 9/20)
      • Pickering, M. and Garrod, S., An integrated theory of language production and comprehension, Behavioral and Brain Sciences, 2012. (in press)
      • Clark, A., Whatever next? Predictive brains, situated agents, and the future of cognitive science, Behavioral and Brain Sciences, 2012. (in press)
      		 3-1

      3-2
      		 3-1

      3-2
      Sang-woo Lee

      Eun-seok Lee

      Week 4

      (9/25, 9/27)
      • Fry, R. L., Neural statics and dynamics, Neurocomputing, 65-66:455-462, 2005.
        (Ref : Fry, R. L., Computation by neural and cortical systems, BMC Neuroscience, 9(Suppl 1):P66, 2008.)
      • Reviews and discussion (9/27)
      		 4-1
      		 4-1-1
      4-1-2

      		 Sungjoo Ha, MyoungHoon Ha

      Week 5

      (10/2, 10/4)
      • Friston, K., The free-energy principle: a rough guide to the brain?, Trends in Cognitive Sciences, 2009.
      • Dayan, P. & Daw, N. D., Connections between computational and neurobiological perspectives on decision making, Cognitive, Affective, & Behavioral Neuroscience, 8(4):429-453, 2008.
      		 5-1

      5-2
      		 5-1

      5-2
      		 Poudel Gokrna

      Daeseob Lim

      Week 6

      (10/9, 10/11)
      • Zahedi, K., Ay,N., and Der, R., Higher coordination with less control-A result of information maximization in the sensorimotor loop, Adaptive Behavior, 18(3-4):338-355, 2010.
      • Tishby, N. & Polani, D., Information theory of decisions and actions, In: Perception-Reason-Action Cycle: Models, Algorithms and Systems, 2010.
      		 6-1

      6-2
      		 6-1

      6-2
      		 Sangam Uprety

      Jongki Cho, Shinchoo Kim

      Week 7

      (10/16, 10/18)
      • Reviews and discussion (10/16)
      • Exam 1 (10/18)
      		  
      		 

      Week 8

      (10/23, 10/25)
      • Bayesian inference and estimation
      • Barber, D., Cemgil, A, T. and Chiappa, S., Inference and estimation in probabilistic time series models [1]
      		   8-1

      8-2

      		  

      Week 9

      (10/30, 11/1)

      • Barber, D., Cemgil, A, T. and Chiappa, S., Inference and estimation in probabilistic time series models [1]
      		  
      		 

      Week 10

      (11/6, 11/8)
      • Haykin, S., Kalman filters [2]
      • Extensions and variations
      		   10-1

      		  

      Week 11

      (11/13, 11/15)
      • Pouget, A. Dayan, P., & Zemel, R. S., Inference and computation with population codes, Annual Review of Neuroscience, 26:381-410, 2003.
      • 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.
      		 11-1 11-1

      11-2
      		 Jisu Kim, Hahn SangWook

      Chung-Yeon Lee, Zhiqiang Ma

      Week 12

      (11/20, 11/22)
      • A tutorial on particle filters [3]
      • Bayesian evolutionary algorithms
      		 Kalman Filter

      Particle Filter       		12-1

      		  

      Week 13

      (11/27, 11/29)
      • Reviews and discussion (11/27)
      • Exam 2 (11/29)
      		  
      		 

      Week 14

      (12/4, 12/6)
      • Osborne, L. C., Palmer, S. E., Lisberger, S. G., and Bialek, W., The neural basis for combinatorial coding in a cortical population response, Journal of Neuroscience, 28,13522-13531, 2008.
      • Bach, D. and Dolan, R. J., Knowing how much you don't know: a neural organization of uncertainty estimates, Nature Reviews Neuroscience, 13:572-586, 2012.
      		   14-1

      14-2
      		 Roger Paris

      Jonathan Boellke

      Week 15

      (12/11, 12/13)
      • Reviews and discussion
      		  

      12/13

      Place: 302-308
    This page is maintained by Sang-Woo Lee
    Last update: 2012.11.21.