SESSION 6: BIOLOGICALLY-INSPIRED COMPUTATIONAL MODELS

Introduction: O'Reilly, R. C., & Munakata, Y. (2003). Computational neuroscience and cognitive modeling. In L. Nadel (Ed.) Encyclopedia of Cognitive Sciences, London: Macmillian

Papers

• O'Reilly, R. C. (1998). Six principles for biologically based computational models of cortical cognition. Trends in Cognitive Sciences, 2, 455-461. (Bethany Schneider)
• McClelland, J. L. (2006). How Far Can You Go with Hebbian Learning, and When Does it Lead you Astray? In Munakata, Y. and Johnson, M. H. (Eds.) Processes of Change in Brain and Cognitive Development: Attention and Performance XXI. pp. 33-69. Oxford: Oxford University Press. (Vsevolod Kapatsinski)
• Hinton, G. E. (2005). What kind of a graphical model is the brain? International Joint Conference on Artificial Intelligence 2005, Edinburgh. (Toshiyuki Uchino)

Suggested Background Readings

• O'Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press.

• Anderson, J. A. (1995). An Introduction to Neural Networks. MIT Press.

(I have both these books, if the library copies are out)

Resources

• PDP++ Software Homepage
• SNNAP Software Homepage
• Randy O'Reilly’s Computational Cognitive Neuroscience Lectures
• Neuron Code (Sejnowski Lab @ UCSD)
• Hinton: Matlab code for deep autoencoder

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