CPSC689-603: Reading List

1. How to read the literature

1. Types of Publications

   In this short note, I talk about different kinds of publications and how to identify them.

2. Developing a critical reading skill

   In this note, I talk about how to develop a critical reading skill that will help you grasp more ideas and spend less time on mediocre ones.

2. Literature Search Resources

1. TAMU Library Catalog

   Your best local resource.

2. Google Scholar

   Simply the best publication/citation search engine.

3. SCIRUS by Elsevier

   Excellent literature search (mostly Elsevier journals).

4. Net Library

   Whole books on the web: a lot of recent books from MIT Press, etc. are available.

5. Good book review sites:
   • The Bactra Review: Cosma Shalizi's excellent book review site (a lot of relevant books to neuroscience and AI).
   • Danny Yee's Book Reviews: a good review site with a broader topic.

6. Good subject review page:
   • Cosma Shalizi's Notebooks: a very good collection of insightful notes on relevant topics.

7. Finding the paper not linked here:

   You can use a search engine. Even if you cannot find the exact same paper you are after, most of the case you can find an earlier version or a similar version by looking up the authors' homepage on the web. This is a crucial tip that you should always keep in mind.

3. Recommended Tutorials and Web Resources

Review Journals

• Nature Reviews: Neuroscience
• Trends in Cognitive Science (tamu access)
• Trends in Neuroscience (tamu access)
• Annual Review of Neuroscience

Encyclopedia, handbooks, and overviews

• Stanford Encyclopedia of Philosophy. Some entries of interest include:

  action, behaviorism, cognitive science, connectionism, category theory, causation (causal processes), causation (probabilistic), identity theory of mind (mind==brain state), mental representation, philosophy of neuroscience,

• Tony Veal's Survey of the Metaphor Field: an excellent review of current and past work. Search for these links within:
  1. Lakoff
  2. Searle
  3. Cognitive scientists
• Chris Eliasmith's Dictionary of Philosophy of Mind: an excellent resource for fundamental issues in AI. Specific entries include the following:
  1. Analogy
  2. Artificial Intelligence
  3. Chinese Room
  4. Content
  5. Representation
  6. Semantics.

4. Main Reading List

Choe et al.

• Yoonsuck Choe. The role of temporal parameters in a thalamocortical model of analogy. IEEE Transactions on Neural Networks, 15:1071-1082, 2004.

• Yoonsuck Choe and S. Kumar Bhamidipati. Autonomous acquisition of the meaning of sensory states through sensory-invariance driven action. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 176-188, Berlin, 2004. Springer.

• Yoonsuck Choe and Risto Miikkulainen. Contour integration and segmentation in a self-organizing map of spiking neurons. Biological Cybernetics, 90:75-88, 2004.

• Sejong Oh and Yoonsuck Choe. Texture segmentation in 2D vs. 3D: Did 3D developmentally precede 2D?. In J. Triesh, T. Jebara, M. S. Bartlett, and G. F. Littlewort, editors, Proceedings of the Third International Conference on Development and Learning (ICDL'04) Developing Social Brains, 2004.

• Yingwei Yu and Yoonsuck Choe. Angular disinhibition effect in a modified Poggendorff illusion. In Kenneth D. Forbus, Dedre Gentner, and Terry Regier, editors, Proceedings of the 26th Annual Conference of the Cognitive Science Society, pages 1500-1505, 2004.

• Yingwei Yu, Takashi Yamauchi, and Yoonsuck Choe. Explaining low-level brightness-contrast illusions using disinhibition. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 166-175, Berlin, 2004. Springer.

• Yoonsuck Choe. Processing of analogy in the thalamocortical circuit. In Proceedings of the International Joint Conference on Neural Networks, pages 1480-1485. IEEE, 2003.

• Hyeon-Cheol Lee and Yoonsuck Choe. Detecting salient contours using orientation energy distribution. In Proceedings of the International Joint Conference on Neural Networks, pages 206-211. IEEE, 2003.

• Yoonsuck Choe. Second order isomorphism: A reinterpretation and its implications in brain and cognitive sciences. In Wayne D. Gray and Christian D. Schunn, editors, Proceedings of the 24th Annual Conference of the Cognitive Science Society, pages 190-195. Erlbaum, 2002.

Perspectives

• Andy Clark and Chris Eliasmith. Philosophical issues in brain theory and connectionism. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 886-888. MIT Press, Cambridge, MA, 2nd edition, 2003.

• Rodney Brooks. The relationship between matter and life. Nature, 409:409-411, 2001.

• Paul R. Cohen and Carole R. Beal. Natural semantics for a mobile robot. Technical Report 00-59, University of Massachusettes, Department of Computer Science, 2000.

• Anthony J. Bell. Levels and loops: The future of artificial intelligence and neuroscience. Philosophical Transactions of the Royal Society of London, 354:2013-2020, 1999.

• R. L. Gregory, editor. The Oxford Companion to the Mind. Oxford University Press, Oxford, 1987.

• Hubert L. Dreyfus. What Computers Can't Do: The Limits of Artificial Intelligence. Harper & Row, New York, revised edition, 1979.

Neuroscience Basics

• R. L. Gregory, editor. The Oxford Companion to the Mind. Oxford University Press, Oxford, 1987.

• Charles F. Stevens. The neuron. In Richard F. Thompson, editor, Progress in Neuroscience: Readings from Scientific American, chapter 1, pages 5-15. Scientific American, 1986.

Thalamus and Basal Ganglia

• Sean Hill and Guilio Tononi. Thalamus. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 1176-1180. MIT Press, Cambridge, MA, 2nd edition, 2003.

• R. W. Guillery and S. Murray Sherman. Thalamic relay functions and their role in corticocortical communication: Generalizations from the visual system. Neuron, 33:163-175, 2002.

• T. J. Prescott, K. Gurney, F. Montes-Gonzalez, M. Humphries, and P. Redgrave. The robot basal ganglia: Action selection by an embedded model of the basal ganglia. In L. F. B. Nicholson and R. Faulls, editors, Basal Ganglia VII. Plenum Press, New York, 2002.

Prefrontal Cortex; Orbitofrontal Cortex

• Joaquín M. Fuster. The prefrontal cortex -- An update: Time is of the essence. Neuron, 30:319-333, 2001.

• Edmund T. Rolls. The function of the orbitofrontal cortex. Neurocase, 5:301-312, 1999.

• E. T. Rolls and L. L. Baylis. Gustatory, olfactory, and visual convergence within the primate orbitofrontal cortex. Journal of Neuroscience, 14:5437-5452, 1994.

Causality; Algebra for Action

• Judea Pearl. Reasoning with cause and effect. AI Magazine, 23:95-111, 2001.

Neural Plasticity

• Sen Song, Kenneth D. Miller, and L. F. Abbott. Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3:919-926, 2000.

Natural Scene Statistics and Neural Coding; Biologically Motivated Vision

• Sejong Oh and Yoonsuck Choe. Texture segmentation in 2D vs. 3D: Did 3D developmentally precede 2D?. In J. Triesh, T. Jebara, M. S. Bartlett, and G. F. Littlewort, editors, Proceedings of the Third International Conference on Development and Learning (ICDL'04) Developing Social Brains, 2004.

• Yingwei Yu and Yoonsuck Choe. Angular disinhibition effect in a modified Poggendorff illusion. In Kenneth D. Forbus, Dedre Gentner, and Terry Regier, editors, Proceedings of the 26th Annual Conference of the Cognitive Science Society, pages 1500-1505, 2004.

• Yingwei Yu, Takashi Yamauchi, and Yoonsuck Choe. Explaining low-level brightness-contrast illusions using disinhibition. In A. J. Ijspeert, M. Murata, and N. Wakamiya, editors, Biologically Inspired Approaches to Advanced Information Technology, Lecture Notes in Computer Science 3141, pages 166-175, Berlin, 2004. Springer.

• Horace Barlow. Redundancy reduction revisited. Network: Computation in Neural Systems, 12:241-254, 2001.

• W. S. Geisler, J. S. Perry, B. J. Super, and D. P. Gallogly. Edge Co-occurrence in natural images predicts contour grouping performance. Vision Research, 41:711-724, 2001.

• Eero P. Simoncelli and Bruno A. Olshausen. Natural image statistics and neural representation. Annual Review of Neuroscience, 24:1193-1216, 2001.

• B. Blakeslee and M. E. McCourt. A multiscale spatial filtering account of the white effect, simultaneous brightness contrast and grating induction. Vision Research, 39:4361-4377, 1999.

• Anthony J. Bell and Terrence J. Sejnowski. The ``independent components'' of natural scenes are edge filters. Vision Research, 37:3327, 1997.

• Horace Barlow. What is the computational goal of the neocortex? In Cristof Koch and Joel L. Davis, editors, Large Scale Neuronal Theories of the Brain, pages 1-22. MIT Press, Cambridge, MA, 1994.

• David J. Field. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 4:2379-2394, 1987.

Bayesian Approach in Perception

• W. Geisler and D. Kersten. Illusions, perception, and bayes. Nature Neuroscience, 5:508-510, 2002.

• Allan D. Jepson and Jacob Feldman. A biased view of perceivers: Commentary on `observer theory, bayes theory, and psychophysics,'. In D. C. Knill and W. Richards, editors, Perception as Bayesian Inference, pages 229-235. Cambridge University Press, Cambridge, UK, 1996.

• D. C. Knill, D. Kersten, and A. Yuille. Introduction: A bayesian formulation of visual perception. In D. C. Knill and W. Richards, editors, Perception as Bayesian Inference, chapter 0, pages 1-22. Cambridge University Press, 1996.

Critique of Computationalism

• Matthias Scheutz, editor. Computationalism: New Directions. MIT Press, Cambridge, MA, 2002.

• Shimon Edelman. Representation and Recognition in Vision. MIT Press, Cambridge, MA, 1999.

• John R. Searle. The explanation of cognition. In John Preston, editor, Thought and Language. Cambridge University Press, Cambridge, UK, 1997. Reprinted in  [searle:book02], Chapter 7.

• R. L. Gregory, editor. The Oxford Companion to the Mind. Oxford University Press, Oxford, 1987.

Analogy/Metaphor and Schemas; Perception and Action; Imitation

• Michael A. Arbib. Language evolution: The mirror system hypothesis. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 606-611. MIT Press, Cambridge, MA, 2nd edition, 2003.

• Michael A. Arbib. Schema theory. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 993-998. MIT Press, Cambridge, MA, 2nd edition, 2003.

• Dedre Gentner and Arthur B. Markman. Analogy-based reasoning and metaphor. In Michael A. Arbib, editor, The Handbook of Brain Theory and Neural Networks, pages 106-109. MIT Press, Cambridge, MA, 2nd edition, 2003.

• Mark Steyvers and Joshua B. Tenenbaum. The large-scale structure of semantic networks: Statistical analysis and a model of semantic growth. working paper, 2002.

• John Demiris and Gillian Hayes. Imitation as a dual-route process featuring predictive and learning components: A biologically plausible computational model. In K. Dautenhahn and C. Nehaniv, editors, Imitation in Animals and Artifacts, chapter 13, pages 327-362. MIT Press, Cambridge, MA, 2001.

• Giacomo Rizzolatti, Leonardo Fogassi, and Vittorio Gallese. Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience, 2:661-670, 2001.

• Barbara Maria Stafford. Visual Analogy: Consciousness as the Art of Connecting. MIT Press, Cambridge, MA, 1999.

• Pentti Kanerva. Dual role of analogy in the design of a cognitive computer. In K. Holyoak, D. Gentner, and B. Kokinov, editors, Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences, pages 164-170. 1998.

• Pentti Kanerva. Large patterns make great symbols: An example of learning from example. NIPS*98 workshop, 1998.

• Douglas S. Blank. Learning to See Analogies: A Connectionist Exploration. PhD thesis, Department of Cognitive Science, Indiana University, Bloomington, IN, 1997.

• Michael A. Arbib. Schema theory: From kant to mcculloch and beyond. In R. Moreno-Diaz and J. Mira-Mira, editors, Brain Processes, Theories and Models: An International Conference in Honor of W.S. McCulloch 25 Years After His Death, pages 11-23. MIT Press, Cambridge, MA, 1996.

• R. L. Gregory, editor. The Oxford Companion to the Mind. Oxford University Press, Oxford, 1987.

Embodied Agents and Multimodal/Sensory-Motor Integration

• J. Weng. Developmental robotics: Theory and experiments. International Journal of Humanoid Robotics, 1:199-236, 2004.

• D. Philipona, J. K. O'Regan, and J.-P. Nadal. Is there something out there? Inferring space from sensorimotor dependencies. Neural Computation, 15:2029-2050, 2003.

• Heinz von Foerster. Understanding Understanding. Springer, New York, 2003.

• Eduardo Alonso. Ai and agents: State of the art. AI Magazine, 23:25-29, 2002.

• Marc O. Ernst and Martin S. Banks. Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415:429-433, 2002.

• J. Weng, J. L. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, and E. Thelen. Autonomous mental development by robots and animals. Science, 291(5504):599-600, 2001.

• Randall D. Beer. Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4:91-99, 2000.

• Paul R. Cohen and Carole R. Beal. Natural semantics for a mobile robot. Technical Report 00-59, University of Massachusettes, Department of Computer Science, 2000.

• Olivier Lebeltel, Pierre Bessìere, Julien Diard, and Emmanuel Mazer. Bayesian robot programming. Technical Report 01, Laboratoire LEIBNIZ, CNRS, 2000.

• Sebastian Thrun. Probabilistic algorithms in robotics. AI Magazine, 21:93-109, 2000.

• H.-M. Gross, A. Heinze, T. Seiler, and V. Stephan. Generative character of perception: A neural architecture for sensorimotor anticipation. Neural Networks, 12:1101-1129, 1999.

• Rodney A. Brooks, Cynthia Braezeal (Ferrell), Robert Irie, Charles C. Kemp, Matthew Marjanovic, Brian Scassellati, and Matthew M. Williamsom. Alternative essences of intelligence. In Proceedings of the 15th National Conference on Artificial Intelligence, pages 961-976, 1998.

• Faustino Gomez and Risto Miikkulainen. 2-D pole-balancing with recurrent evolutionary networks. In Proceedings of the International Conference on Artificial Neural Networks, pages 425-430, Berlin; New York, 1998. Springer-Verlag.

• Ralf Salomon. Achieving robust behavior by using proprioceptive activity patterns. Biosystems, 47:193-206, 1998.

• J. Demiris and G. Hayes. Do robots ape?. In Proceedings of the AAAI Fall Symposium on Socially Intelligent Agents, pages 28-31, 1997.

• David Pierce and Benjamin Kuipers. Map learning with uninterpreted sensors and effectors. Artificial Intelligence, 92:169-227, 1997.

• David Mark Pierce. Map Learning with Uninterpreted Sensors and Effectors. PhD thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995.

• Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski. A critique of pure vision. In Cristof Koch and Joel L. Davis, editors, Large Scale Neuronal Theories of the Brain. MIT Press, Cambridge, MA, 1994.

Unsupervised Learning and Self-Organization

• Yoonsuck Choe and Risto Miikkulainen. Contour integration and segmentation in a self-organizing map of spiking neurons. Biological Cybernetics, 90:75-88, 2004.

• Peter Dayan. Unsupervised learning. In Robert A. Wilson and Frank Keil, editors, The MIT Encyclopedia of the Cognitive Sciences. MIT Press, Cambridge, MA, 1999.

• Colin Fyfe. Trends in unsupervised learning. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN'1999), pages 21-23, 1999.

• Richard Langlois and Rierre Garrouste. Cognition, redundancy, and learning in organizations. Economics of Innovation and New Technology, 4:287-299, 1997.

• H. B. Barlow. Unsupervised learning. Neural Computation, 1:295-311, 1989.

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