Articles in journal or book chapters

1. S. J. Hanson, C. Hanson, Y. O. Halchenko, T. Matsuka, and A. Zaimi. Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action.. Brain Struct Funct, 212(3-4):231-44, 2007. Keyword(s): Attention/physiology, Brain/blood supply/*physiology, *Brain Mapping, Comprehension/*physiology, Humans, Image Processing, Computer-Assisted/methods, Magnetic Resonance Imaging, Models, Neurological, Oxygen/blood, Pattern Recognition, Visual/*physiology, Photic Stimulation/methods, Psychomotor Performance/physiology, Time Factors, *Vision. [Abstract] [bibtex-entry]



2. Yaroslav O. Halchenko, Stephen José Hanson, and Barak A. Pearlmutter. Multimodal Integration: fMRI, MRI, EEG, and MEG. In Luigi Landini, Vincenzo Positano, and Maria Filomena Santarelli, editors, Advanced Image Processing in Magnetic Resonance Imaging, Signal Processing and Communications, chapter 8, pages 223-265. Dekker, . Keyword(s): EEG, MEG, fMRI, MRI, multimodal analysis, fusion. [Abstract] [bibtex-entry]



3. T. Matsuka. Simple, individually unique, and context dependent learning methods for models of human category learning. Behavior Research Methods, 2005. [bibtex-entry]



4. B. M. Bly, D. Rebbechi, S. J. Hanson, and G. Grasso. The RUMBA software: tools for neuroimaging data analysis. Neuroinformatics, 2(1):71-100, 2004. Keyword(s): *Algorithms, Animals, Brain Mapping/*methods, Human, Image Processing, Computer-Assisted/*methods, Magnetic Resonance Imaging/*methods, Programming Languages, Reproducibility of Results, Signal Processing, Computer-Assisted, Software/*standards/trends, Software Validation, Support, Non-U.S. Gov't, User-Computer Interface. [Abstract] [bibtex-entry]



5. S. J. Hanson, M. Negishi, and C. Hanson. Emergent Neural Computational Architectures Based on Neuroscience, chapter Connectionist Neuroimaging. Springer-Verlag, 2001. Keyword(s): neural net, connectionism. [Abstract] [bibtex-entry]



6. S. J. Hanson and B. M. Bly. The distribution of BOLD susceptibility effects in the brain is non-Gaussian. Neuroreport, 12(9):1971-7, 2001. Keyword(s): Brain/*metabolism/*physiology, Brain Mapping/methods, Cerebrovascular Circulation/*physiology, Human, Magnetic Resonance Imaging/methods, Models, Neurological, Normal Distribution, Oxygen/*blood, Support, Non-U.S. Gov't. [Abstract] [bibtex-entry]



7. S. J. Hanson and D. J. Burr. What Connectionist Models Learn: Toward a theory of representation in Connectionist Networks. Behavioral and Brain Sciences, 13:471-518, 1990. [bibtex-entry]

Conference articles

1. T. Matsuka. Comparisons of prototype- and exemplar-based neural network models of categorization using the GECEL framework. In Proceedings of the Annual Meeting of the Cognitive Science Society, pages 909-914, 2004. [bibtex-entry]



2. T. Matsuka and J. E. Corter. Stochastic Learning in neural network models of categorization. In Proceedings of the 25th Annual Meeting of the Cognitive Science Society, Boston, MA, 2003. [bibtex-entry]



3. R. Murthy, B. M. Bly, J. Deutsch, G. Lange, and S. J. Hanson. Human Brain Mapping with ARIMA models. In Proceedings of the Annual Meeting of the Organization for Human Brain Mapping, 1999. [bibtex-entry]

Miscellaneous

1. T. Matsuka. On the selective attention mechanism of prototype models of categorization, 2005. Note: Under review. [bibtex-entry]



2. T. Matsuka, J. E. Corter, and A. B. Markman. Attention learning in adaptive network models of categorization, 2003. Note: Under review. [bibtex-entry]

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