David C. Noelle
University of California, Merced
dnoelle@ucmerced.edu
Computational cognitive neuroscience involves the fabrication, analysis, and evaluation of computational models that attempt to bridge the gap between brain function and overt behavior. This course introduces the concepts and methods of computational cognitive neuroscience using the Leabra modeling framework. Leabra provides an integrated collection of conceptual tools for the construction of cognitive models. These tools will be used to explore the modeling of important biological features of neural systems, such as membrane potential dynamics, rapid shunting lateral inhibition, and biologically realistic mechanisms for synaptic plasticity, as well as the emergent properties of aggregate network dynamics, producing behaviors of psychological relevance. In this way, the models investigated in this course span a middle ground between biophysically detailed neural simulations and cognitive models that are grounded in psychological theory. Leabra has been implemented in the freely available Emergent simulator, which provides a graphical point-and-click interface for constructing, executing, and analyzing computational models. Leabra simulations will be used to introduce issues in computational cognitive neuroscience modeling, providing an introduction to Leabra models of perception, attention, learning, memory, and cognitive control.
Readings
Required readings are all drawn from the following book:
O'Reilly, R. C. & Munakata, Y. (2000) Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press: Cambridge, MA.
In the schedule, below, this book is referenced as "CECN". Additional readings are drawn from the broader literature.
Software
This course will involve extensive demonstrations of Leabra models using the freely available Emergent software. Students are encouraged (though not required) to download this software and explore the demonstration simulations on their own. The Emergent software may be found at:
http://grey.colorado.edu/emergent/
Versions of this software are available for Windows, OS X, and Linux.
Some of the models which will be demonstrated may be found at:
http://grey.colorado.edu/CompCogNeuro/
Note that using Emergent does not require any knowledge of computer programming.
Required readings: CECN, Chapters 1 & 2.
Additional readings:
Jennings, C. & Aamodt, S. (2000) Computational approaches to brain function. Nature Neuroscience, 3(11), p. 1160.
Siegelbaum, S. A. & Koester, J. (1991) Ion channels. Chapter 5 of E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.), Principles of Neural Science: Third Edition. Appleton & Lange: Norwalk, Connecticut.
Koester, J. (1991) Membrane potential. Chapter 6 of E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.), Principles of Neural Science: Third Edition. Appleton & Lange: Norwalk, Connecticut.
Koester, J. (1991) Passive membrane properties of the neuron. Chapter 7 of E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.), Principles of Neural Science: Third Edition. Appleton & Lange: Norwalk, Connecticut.
Koester, J. (1991) Voltage-gated ion channels and the generation of the action potential. Chapter 8 of E. R. Kandel, J. H. Schwartz, & T. M. Jessell (Eds.), Principles of Neural Science: Third Edition. Appleton & Lange: Norwalk, Connecticut.
Required readings: CECN, Chapter 3.
Additional readings:
Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986) Distributed representations. Chapter 3 in in D. E. Rumelhart, J. L. McClelland, & the PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. MIT Press: Cambridge, MA.
Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. E. (1986) Schemata and sequential thought processes in PDP models. Chapter 14 in in J. L. McClelland, D. E. Rumelhart, & the PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychologiical and Biological Models. MIT Press: Cambridge, MA.
Required readings: CECN, Chapters 4, 5, & 6.
Additional readings:
Gluck, M. A. & Bower, G. H. (1988) From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, pp. 227-247.
Hinton, G. E. (1989) Connectionist learning procedures. Artificial Intelligence, 40, pp. 185-234.
Rumelhart, D. E., Durbin, R., Golden, R., & Chauvin, Y. (1995) Backpropagation: The basic theory. Chapter 1 in Y. Chauvin & D. E. Rumelhart (Eds.), Backpropagation: Theory, Architectures, and Applications. Lawrence Erlbaum: Hillsdale, NJ.
Sutton, R. S. (1988) Learning to predict by the method of temporal differences. Machine Learning, 3, pp. 9-44.
Barto, A. G. (1994) Adaptive critics and the basal ganglia. Chapter 11 in J. C. Houk, J. L. Davis, & D. G. Beiser (Eds.), Models of Information Processing in the Basal Ganglia. MIT Press: Cambridge, MA.
Required Readings: CECN, Chapters 7 & 8.
Additional readings:
McClelland, J. L., McNaughton, B. L., and O'Reilly, R. C. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, pp. 419-457.
Rolls, E. T. & Deco, G. (2001) Computational Neuroscience of Vision. Oxford University Press.
Mozer, M. C. & Sitton, M. (1998) Computational modeling of spatial attention. In H. Pashler (Ed.), Attention, pp. 341-393. UCL Press: London.
Itti, L. & Koch, C. (2001) Computational modeling of visual attention. Nature Reviews Neuroscience, 2, pp. 194-203.
Required Readings: CECN, Chapters 9 & 11.
Additional readings:
Norman, K. A. & O'Reilly, R. C. (2003) Modeling hippocampal and neocortical contributions to recognition memory: A complementary-learning-systems approach. Psychological Review, 110, pp. 611-646.
Cohen, J. D. & Servan-Schreiber, D. (1992) Context, cortex, and dopamine: A connectionist approach to behavior and biology in schizophrenia. Psychological Review, 99, pp. 45-77.
O'Reilly, R. C., Noelle, D. C., Braver, T. S., & Cohen, J. D. (2002) Prefrontal cortex and dynamic categorization tasks: Representational organization and neuromodulatory control. Cerebral Cortex, 12, pp. 246-257.
Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O'Reilly, R. C. (2005) Prefrontal cortex and the flexibility of cognitive control: Rules without symbols. Proceedings of the National Academy of Science, 102(20), pp. 7338-7343.
O'Reilly, R. C. & Frank, M. J. (2006) Making working memory work: A computational model of learning in the frontal cortex and basal ganglia. Neural Computation, 18, pp. 283-328.
Assessment
Student mastery of the course material will be assessed through the evaluation of a required written project report. This short report (approximately five pages) will either (1) report on a detailed analysis of one of the computational models presented during the course, or (2) will offer a detailed proposal for using Leabra to model a cognitive phenomenon of specific interest to the student.
David C. Noelle is Assistant Professor of Cognitive Science and Computer Science at the University of California, Merced. He received his Ph.D. in Cognitive Science & Computer Science from the University of California, San Diego, under the supervision of Gary Cottrell. He completed postdoctoral training with James McClelland and Jonathan Cohen at the Center for the Neural Basis of Cognition, a joint project of Carnegie Mellon University and the University of Pittsburgh. Prior to moving to Merced, Dr. Noelle was on the faculty at Vanderbilt University. Dr. Noelle's research focuses on computational cognitive neuroscience models of the role of prefrontal cortex in learning, memory, and cognitive control. He has also worked on translating brain models into robot control systems. Current projects include computational models of learning deficits in autism and the effects of stress on episodic memory.