A COMPUTATIONAL APPROACH FOR STUDYING THE BRAIN

描述（改编自申请人的摘要）：主要 这个应用程序的前提是，有足够的学习能力 记忆是正常认知行为的基础， 一个小前提是，许多认知行为取决于 陈述性记忆需要海马体来进行适当的 存储.从这些前提出发， 理解海马体最初是如何形成陈述性的 然后与大脑皮层相互作用， 长期记忆更直接的目标是提供一个 通过模拟海马功能的定量理解 生物学上合理的类似露营的认知网络， 需要海马体来维持正常的行为状态 功能这种模拟以一种特别令人惊讶的方式显示， 对训练程序的敏感性与老鼠和人类一样。 也就是说，该模型预测了个体学习的分布， 表演因此，这些模型可用于开发 最佳的学习/培训程序，以提高穷人的 学习者 使用三个范式学习问题和一系列 密切相关的，最小的，生物学上合理的模型， 海马，这项建议的具体目标是：1） 了解海马体的信息处理，包括其 关键生物基质; 2）对于每种学习范式， 预测海马细胞放电的模式， 在学习过程中的休息时间， 在学习后的测试中; 3）基于个人 由模型的参数化生物学引起的差异， 来解释行为表现的个体差异， 动物;和4）证明（或改善，如果不成功） 模型的可行性，通过预测新的行为结果， 训练情况。 计算机模拟将进行跟踪调节和 传递性推理和横向推理两种认知范式 模式化利用数学分析和简化模型， 海马体，在复杂性， 和参数化，申请人将试图了解如何 海马体解剖学和相关生理学的原型 复制海马体的功能[包括 上下文形成和灵活的记忆表征（Eichenbaum 例如，'92）]以及为什么其他一些生物学和参数化 失败

DESCRIPTION (Adapted From The Applicant's Abstract): The major premise of this application is that an adequate ability to learn and to remember is fundamental to normal cognitive behavior and the minor premise is that many cognitive behaviors hinge upon declarative memories that require a hippocampus for appropriate storage. From these premises follow the long term goal of understanding how the hippocampus initially forms declarative memories and then interacts with cerebral cortex in the storage of long-term memories. The more immediate goal is to provide a quantitative understanding of hippocampal function by simulating biologically plausible hippocampal-like networks in cognitive- behavioral situations that require the hippocampus for their normal function. Such simulations, in a particularly surprising way, show the same sensitivity to training procedures as do rats and humans. Namely the model predicts the distribution of individual learned performances. Such models might therefore be used to develop optimal learning/training procedures to improve the poorer learners. Using three paradigmatic learning problems and a spectrum of closely related, minimal, biologically plausible models of the hippocampus, the specific aims of this proposal are: 1) to understand information processing in the hippocampus including its critical biological substrates; 2) for each learning paradigm, to predict the patterns of hippocampal cell firing that occur during learning, during rest periods over the course of learning, and during testing after learning; 3) based on the individual differences that arise from the parameterized biology of the model, to explain the individual differences of behavioral performance in animals; and 4) to prove (or improve, if unsuccessful) the viability of the model by predicting behavioral outcomes in novel training situations. Computer simulations will be performed of trace conditioning and of two cognitive paradigms--transitive inference and transverse patterning. Using mathematical analyses and simplified models of the hippocampus, which are systematically varied in both complexity and parameterization, the applicant will attempt to understand how the archetypal hippocampal anatomy and associated physiologies reproduce the functions ascribed to the hippocampus [including context formation and flexible memory representations (Eichenbaum et al., '92)] and why some other biologies and parameterizations fail.