Computational Core

计算核心中的详细建模将解决有关本研究中特定项目的假设。 格兰特，连接细胞，系统和行为水平。我们的核心分析工具将是 在上一个资助期开发的大脑内嗅皮层模型。我们将增加其 生物现实主义的程度，以适应个人产生的新的研究成果 项目其目的是阐明海马病因的精神分裂症在一个中间或 “内表型”水平。因为重要的是我们的模型表现出临床上可验证的行为 并捕捉精神分裂症的重要症状特征，一些心理测试将被 用作精神分裂症与正常功能的测定，包括加州言语学习任务 （CVLT）、带有语义线索的修正自由回忆任务、配对联想（PA）任务和交叉任务。 序列（CS）任务。迄今为止，我们所做的与这项赠款有关的计算工作表明， 未能适当地利用上下文是精神分裂症的核心认知异常; 作为CVLT的一部分的聚类指数利用这种认知功能。PA任务被认为是索引 一般来说，陈述性记忆功能，精神分裂症患者一直表现出困难。 人们认为，无法回忆和处理顺序信息，如CS缺陷所揭示的 任务，可能是精神分裂症思维的切线和松散特征。经过培训， 在这些任务上，我们将使它受到许多神经生理学或神经解剖学异常的影响。 被认为引起或促成精神分裂症（例如，中间神经元缺陷和相关连接 干扰，由于NAAG水平异常、多巴胺水平过高而导致的NMDA活性降低）。 我们预测，肾上腺素的变化将导致模型在 上述心理任务。然后，我们将检查模型行为来检验假设， 在赠款的个别项目中描述，关于细胞水平的机制， 异常相互作用产生临床症状。即使特定的假设机制不 似乎是可操作的，这也是有指导意义的-因为生物学上的现实模拟是完全可行的。 透明的，它的行为可以产生新的假设，然后可以通过实验进行测试。

Detailed modeling in the Computational Core will address hypotheses concerning the specific projects in this grant, linking the cellular, systems, and behavioral levels. Our central analytical tool will be the hippocampus-entorhinal cortex model that was developed in the last funding period. We will increase its degree of biological realism in order to accommodate new research findings generated by the individual projects. Its purpose is to shed light on the hippocampal etiology of schizophrenia at an intermediate or "endophenotype" level. Because it is important that our model manifest behaviors that are clinically verifiable and that capture important symptomatic features of schizophrenia, a number of psychological tests will be used as assays of schizophrenia vs. normal functioning, including The California Verbal Learning Task (CVLT), modified free recall tasks with semantic cueing, the paired associate (PA) task, and the crossing sequences (CS) task. The computational work related to this grant we have done to date has indicated that a failure to appropriately utilize context is a core cognitive abnormality in schizophrenia; the category clustering indices that are part of the CVLT tap this cognitive function. The PA task is thought to index declarative memory function generally, and schizophrenic patients have consistently shown difficulties with it. It is thought that inability to recall and process sequential information, as revealed by deficienies in the CS task, may underlay the tangentiality and looseness characteristic of schizophrenic thought. After training the network on these tasks, we will subject it to the the many neurophysiologic or neuroanatomic abormalities thought to cause or contribute to schizophrenia (e.g., interneuron deficiencies and related connectivity disturbances, decreased NMDA activity as a result of abnormal levels of NAAG, excessive dopamine levels). We predict that schizophrenogenic changes will produce decreased performance of the model on the aforementioned psychological tasks. We then will examine model behaviors to test hypotheses, as described in the individual projects of the grant, concerning the mechanisms by which the cellular level abnormalities interact to produce clinical symptoms. Even if particular hypothesized mechanisms do not appear to be operative, this is instructive also--as biologically realistic in silico modeling is completely transparent, its behaviors can generate new hypotheses which can then be tested experimentally.