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

Bridging Levels of Understanding in Schizophrenia Through Computational Modeling.

Author information

1
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.
2
Center for Neural Science, New York University.
3
Department of Psychology and Department of Psychiatry, Washington University in St. Louis.

Abstract

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.

KEYWORDS:

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

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