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Clin Psychol Sci. 2015 May;3(3):433-459.

Bridging Levels of Understanding in Schizophrenia Through Computational Modeling.

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Department of Psychiatry, Yale University ; National Institute on Alcohol Abuse and Alcoholism Center for the Translational Neuroscience of Alcoholism, New Haven, Connecticut ; Abraham Ribicoff Research Facilities, Connecticut Mental Health Center, New Haven.
Center for Neural Science, New York University.
Department of Psychology and Department of Psychiatry, Washington University in St. Louis.


Schizophrenia is an illness with a remarkably complex symptom presentation that has thus far been out of reach of neuroscientific explanation. This presents a fundamental problem for developing better treatments that target specific symptoms or root causes. One promising path forward is the incorporation of computational neuroscience, which provides a way to formalize experimental observations and, in turn, make theoretical predictions for subsequent studies. We review three complementary approaches: (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and (c) models that provide a formalism for observations of complex behavioral deficits, such as negative symptoms. We argue that harnessing all of these modeling approaches represents a productive approach for better understanding schizophrenia. We discuss how blending these approaches can allow the field to progress toward a more comprehensive understanding of schizophrenia and its treatment.


cognitive deficits; computational modeling; schizophrenia; symptoms; systems neuroscience

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