Moustafa, A. A. & Gluck, M. A. (2011). Computational cognitive models of prefrontal-striatal-hippocampal interactions in Parkinson's disease and schizophrenia. Neural Networks. In press (doi:10.1016/j.neunet.2011.02.006)

Moustafa, A. A., Keri, S., Herzallah, M. M., Myers, C. E., & Gluck, M. A. (2010). A neural model of hippocampal–striatal interactions in associative learning and transfer generalization in various neurological and psychiatric patients Brain and Cognition, 74, 132–144.

Moustafa, A. A., & Gluck, M. A. (2011). A Neurocomputational Model of Dopamine and Prefrontal-Striatal Interactions during Multicue Category Learning by Parkinson's Patients. J Cogn Neurosci.

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Moustafa, A. A., Myers, C. E., & Gluck, M. A. (2009). A neurocomputational model of classical conditioning phenomena: a putative role for the hippocampal region in associative learning. Brain Res, 1276, 180-195.

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Guthrie, M., Myers, C. E., & Gluck, M. A. (2009/In press). A neurocomputational model of tonic and phasic dopamine in action selection: A comparison with cognitive deficits in Parkinson’s disease. Behavioral Brain Research.

Gluck, M. A., Myers, C. E., Nicolle, M. M. & Johnson, S. (2006). Computational models of the hippocampal region: Implications for prediction of risk for Alzheimer's disease in non-demented elderly. Current Alzheimer's Research. 3. 247-257.

Meeter, M., Myers, C. E., & Gluck, M. A. (2005). Integrating incremental learning and episodic memory models of the hippocampal region. Psychological Review. 112(3). 560-585.

Gluck, M. A., Meeter, M., & Myers, C. E. (2003). Computational models of the hippocampal region: Linking incremental learning and episodic memory. Trends in Cognitive Science. 7(6). 269-276.

Tijsseling, A. G. & Gluck, M. A. (2002). Processing integral and separable dimensions in category learning: A connectionist perspective. Connection Science. 14(1). 1-48.

Rokers, B., Mercado, E., Allen, M. T., Myers, C. E. & Gluck, M. A. (2002). A connectionist model of septohippocampal dynamics during conditioning: Closing the loop. Behavioral Neuroscience. 116(1), 48-62.

Mercado, E., III, Myers, C., Gluck, M. (2001).  A computational model of mechanisms controlling experience-dependent reorganization of representational maps in auditory cortex. Cognitive, Affective and Behavioral Neuroscience, 1(1), 37-55.

Gluck, M. A. & Myers, C. E. (2001). Gateway to Memory: An Introduction to Neural Network Models of the Hippocampus and Learning. Cambridge, MA: MIT Press.

Mercado, E., III, Myers, C. E. & Gluck, M.A. (2000). Modeling auditory cortical processing as an adaptive chirplet transform. Neurocomputing, 32-33(1-4), 913-919.

Rokers, B., Myers, C.E, & Gluck, M.A. (2000). A dynamic model of learning in the septo-hippocampal system. Neurocomputing. 32-33(1-4). 501-507.

Japkowicz, N., Hanson, S.J., & Gluck, M.A., (2000). Nonlinear autoassociation is not equivalent to PCA. Neural Computation. 12(3). 531-545.

Myers, C., Ermita, B., Hasselmo, M. & Gluck, M. (1998).  Further implications of a computational model of septohippocampal cholinergic modulation in eyeblink conditioning.  Psychobiology, 26(1), 1-20.

Gluck, M. A., Ermita, B. R., Oliver, L. M., & Myers, C. E. (1997).  Extending models of hippocampal function in animal conditioning to human amnesia. Memory. 5 (1/2), 179-212.

Gluck, M. A., & Myers, C. E. (1997). Psychobiological models of hippocampal function in learning and memory. Annual Review of Psychology. 48. 481-514.

Myers, C. E., & Gluck, M. A.  (1996). Cortico-hippocampal representations in simultaneous odor discrimination: A computational interpretation of Eichenbaum, Mathews, and Cohen (1989). Behavioral Neuroscience. 110(4), 1-22.

Myers, C. E., Ermita, B., Harris, K., Hasselmo, M., Solomon, P., & Gluck, M. A. (1996).  A computational model of the effects of septohippocampal disruption on classical eyeblink conditioning. Neurobiology of Learning and Memory, 66, 51-66.

Myers, C. E., Gluck, M. A., & Granger R. (1995). Dissociation of hippocampal and entorhinal function in associative Learning: A computational approach. Psychobiology 23(2). 116-138.

Myers, C. E. & Gluck, M. A. (1994). Context, conditioning and hippocampal re-representation. Behavioral Neuroscience.108(5), 835-847.

Shanks, D. R. & Gluck, M. A. (1994). Tests of an adaptive network model for the identification, categorization, and recognition of continuous-dimension stimuli. Connection Science. 6(1), 59-89.

Gluck, M. A. & Myers, C. (1993). Hippocampal mediation of stimulus representation: A computational theory. Hippocampus. 3(4): 491-516

Gluck, M. A., & Granger, R. (1993). Computational models of the neural bases of learning and memory. Annual Review of Neuroscience. 16, 667-706.

Corter, J. E., & Gluck, M. A. (1992) Explaining basic categories: Feature predictability and information. Psychological Bulletin. 111(2), 291-303.

Gluck, M. A. (1991). Stimulus generalization and representation in adaptive network models of category learning. Psychological Science, 2(1), 50-55.

Gluck, M. A. & Bower, G. H. (1990).  Component and pattern information in adaptive networks.  Journal of Experimental Psychology: General, 119(1), 105-109.

Donegan, N. H., Gluck, M. A., & Thompson, R. F. (1989).  Integrating behavioral and biological models of classical conditioning.  Psychology of Learning and Motivation (Volume 22). New York: Academic Press, 109-156.

Gluck, M. A. & Bower, G. H. (1988).  From conditioning to category learning: An adaptive network model.  Journal of Experimental Psychology: General, 117(3), 227-247.

Gluck, M. A., & Bower, G. H. (1988). Evaluating an adaptive network model of human learning. Journal of Memory and Language, 27, 166-195.

Gluck, M. A., Parker, D. B., & Reifsnider, E. (1988).  Some biological implications of a differential-Hebbian learning rule. Psychobiology, 16(3), 298-302.

Gluck, M. A. & Thompson, R. F. (1987).  Modeling the neural substrates of associative learning and memory: A computational approach, Psychological Review, 94(2), 176-191.