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通过计算机模拟的精神分裂症的逐级认识

  Bridging Levels of Understanding in Schizophrenia Through Computational Modeling

  Clin Psychol Sci. Author manuscript; available in PMC 2015 May 6.

  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4421907/

  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: computational modeling, schizophrenia, symptoms, cognitive deficits, systems neuroscience