Halchenko, Y.O., Hanson, S.J. & Pearlmutter, B. A. (2005). Multimodal Integration: fMRI MRI, EEG, MEG, in Advanced Image Processing in Dekker (Ed.), Magnetic Resonance Imaging. Boston, MA:MIT Press:. (pp. 223-266). Hanson, S.J. (1990). Meiosis Networks. In D.S. Touretzky (Ed.), Advances in Neural Information Processing Systems 2, 533-541. Hanson, S. J., (1990), Conceptual Clustering and Categorization: Bridging the Gap between Induction and Causal Models, in Y. Kodratoff & R. Michchalski (Eds.)Machine Learning: An Artificial Intelligence Approach, Vol III, , Morgan Kaufmann, (pp. 235-269). Hanson, S. J., (1991), Behavioral Diversity, Search, and Stochastic Connectionist Systems, in M. Commons, S. Grossberg & J. Staddon (Eds.), Neural Network Models of Conditioning and Action. Hillsdale, NJ:Erlbaum, (pp. 295-345). Hanson, S. J., (1995), Backpropagation: Some comments and variations. In Y. Chauvin & D. Rummelhart (Eds.), Backpropagation: Theory, architectures, and applications. Hillsdale, NJ: Erlbaum, (pp. 237-272). Hanson, S. J. (2000). Connectionist Neuroscience: Representational and learning issues for neuroscience. In E. Lepore , Z. Pylyshyn (Eds.), What is Cognitive Science? Malden, MA: Blackwell (pp 401-428). Hanson, S.J. and Burr, D.J. (1987). Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals.
In D.Z. Anderson (Ed.), Neural Information Processing Systems, Melville, NY: American Institute of Physics, (pp. 348-357). Hanson, S. J. and Burr, D. J. (1991). What connectionist models learn: Learning and representation in connectionist networks. In R. J. Mammone and Y. Y. Zeevi(Eds.) Neural Networks: Theory and Applications. San Diego: CA: Academic Press Professional (pp. 169-208). Hanson, S. J. & Gluck, M. A. (1991), Spherical Units as Dynamic Consequential Regions: Implications for Attention, Competition and Categorization, in R.Lippman, J. Moody, & D. Touretzsky, (Eds.), Advances in Neural Information Processing-3, San Francisco, CA: Morgan Kaufmann, (pp. 656-665). response to objects and events. In H. Cohen & C. Lefebvre (Eds.) Handbook on categorization in cognitive science. New York: Elsevier (pp. 119-140). Hanson, S. J. & Kegl, J. (1987). PARSNIP: A connectionist network that learns natural language grammar from exposure to natural language sentences. In Proceedings of the Ninth Conference of the Cognitive Science Society. Seattle, 106-119. Hanson, S. J. & Pratt, L. Y. (1988), Some Comparisons of Constraints on Minimal Networks with Back-Propagation. In D. Touretzky (Ed.)Advances in NeuralInformation Processing-1, San Francisco, CA: Morgan Kaufmann, (pp. 177-186). Hanson, S. J., Negishi, M. & Hanson C. (2001), Connectionist Neuroimaging, In S. Wertmer (Ed.), Lecture Notes in Computer Science: Emergent Neural Computational Architectures Based on Neuroscience. Berlin: Springer (pp. 560-576). Hanson,
S.J., Remmele,
W., Ronald, and Rivest, L. (1993). Strategic Directions in Machine Learning.
Machine Learning: From Theory to Applications. In S.J. Hanson, W. Remmele, and L. Rivest (Eds.), Machine Learning: From Theory to Applications. Berlin: Springer (pp. 1-4). Harnad, S. Hanson, S. J. & Lubin, J. (1991), Categorical Perception and the Evolution of Supervised Learning in Neural Networks, In D. W. Powers & L. Reeker (Eds.) Working Papers of the AAAI Spring Symposium on Machine Learning of Natural Language and Ontology (pp. 65-74). Harnad, S., Hanson, S. J. & Lubin, J., (1995). Learned Categorical Perception in Neural Networks: Implications for Symbol Grounding, In V. Honavar and L. Uhr (Eds.), Symbol Processing and Connectionist Network Models in Artificial Intelligence and Cognitive Modeling : Steps Toward Principled Integration. Hillsdale, NJ: Erlbaum, pp.191-206. Matsuka, T., Yamauchi, T., Hanson, C, and Hanson, S.J. (2005). Representing Categorical Knowledge: An fMRI Study. In Proceedings of the 27th Annual Meeting of the Cognitive Science Society (pp. 1821-1826). Mahwah, NJ: Lawrence Erlbaum. Woodruff-Pak, D.S. & Hanson, C. (1995). Compensating for psychological deficits and declines. In R.A. Dixon & L.Backman (Eds.), Plasticity and compensation in brain memory systems in aging Hillsdale, NJ: Erlbaum (pp. 191-217). |
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