title: Bridging Levels of Understanding in Schizophrenia Through Computational Modeling.
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.
authors.affiliation: 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.
authors.forename: Alan
authors.initials: A
authors.lastname: Anticevic
authors.affiliation: Center for Neural Science, New York University.
authors.forename: John D
authors.initials: JD
authors.lastname: Murray
authors.affiliation: Department of Psychology and Department of Psychiatry, Washington University in St. Louis.
authors.forename: Deanna M
authors.initials: DM
authors.lastname: Barch
keywords: cognitive deficits; computational modeling; schizophrenia; symptoms; systems neuroscience