Silverstein Steven M, Wibral Michael, Phillips William A
Division of Schizophrenia Research, Rutgers University, Piscataway, New Jersey.
MEG Unit, Brain Imaging Center, Goethe University, Frankfurt, Germany.
Comput Psychiatr. 2017 Oct 1;1:82-101. doi: 10.1162/CPSY_a_00004. eCollection 2017 Oct.
Information theory provides a formal framework within which information processing and its disorders can be described. However, information theory has rarely been applied to modeling aspects of the cognitive neuroscience of schizophrenia. The goal of this article is to highlight the benefits of an approach based on information theory, including its recent extensions, for understanding several disrupted neural goal functions as well as related cognitive and symptomatic phenomena in schizophrenia. We begin by demonstrating that foundational concepts from information theory-such as Shannon information, entropy, data compression, block coding, and strategies to increase the signal-to-noise ratio-can be used to provide novel understandings of cognitive impairments in schizophrenia and metrics to evaluate their integrity. We then describe more recent developments in information theory, including the concepts of infomax, coherent infomax, and coding with synergy, to demonstrate how these can be used to develop computational models of schizophrenia-related failures in the tuning of sensory neurons, gain control, perceptual organization, thought organization, selective attention, context processing, predictive coding, and cognitive control. Throughout, we demonstrate how disordered mechanisms may explain both perceptual/cognitive changes and symptom emergence in schizophrenia. Finally, we demonstrate that there is consistency between some information-theoretic concepts and recent discoveries in neurobiology, especially involving the existence of distinct sites for the accumulation of driving input and contextual information prior to their interaction. This convergence can be used to guide future theory, experiment, and treatment development.
信息论提供了一个形式框架，在这个框架内可以描述信息处理及其紊乱情况。然而，信息论很少被应用于对精神分裂症认知神经科学的各个方面进行建模。本文的目的是强调基于信息论的方法（包括其最近的扩展）在理解精神分裂症中几种受损的神经目标功能以及相关认知和症状现象方面的益处。我们首先证明，信息论的基础概念，如香农信息、熵、数据压缩、分组编码以及提高信噪比的策略，可用于对精神分裂症的认知障碍提供新的理解，并用于评估其完整性的指标。然后，我们描述信息论的最新进展，包括信息最大化、相干信息最大化和协同编码的概念，以展示如何利用这些概念来开发与精神分裂症相关的感觉神经元调谐、增益控制、知觉组织、思维组织、选择性注意、情境处理、预测编码和认知控制失败的计算模型。在整个过程中，我们展示了紊乱机制如何解释精神分裂症中的感知/认知变化和症状出现。最后，我们证明了一些信息论概念与神经生物学的最新发现之间存在一致性，特别是涉及驱动输入和情境信息在相互作用之前积累的不同位点的存在。这种趋同可用于指导未来的理论、实验和治疗发展。
