A COMPUTATIONAL APPROACH FOR STUDYING THE BRAIN

描述(改编自申请人的摘要)：专业 这个应用的前提是有足够的学习能力 而记忆是正常认知行为的基础， 次要的前提是，许多认知行为取决于 陈述性记忆需要海马体才能适当 储藏室。从这些前提出发，我们的长期目标是 理解海马体最初是如何形成陈述性的 记忆，然后与大脑皮质相互作用的存储 长期记忆。更直接的目标是提供一个 通过模拟定量了解海马区的功能 在认知障碍中，生物学上看似合理的海马状网络 需要海马体才能正常工作的行为状况 功能。这种模拟，以一种特别令人惊讶的方式表明， 对训练程序的敏感度与大鼠和人类一样。 也就是说，模型预测了学习到的个体的分布 表演。因此，这种模型可能被用来开发 改善贫困人口的最佳学习/培训程序 学习者。 使用三个范例学习问题和一系列 密切相关的，最小的，生物学上可信的模型 海马区，这项建议的具体目的是：1) 了解海马体中的信息处理，包括其 关键生物底物；2)对于每个学习范例， 预测海马区细胞的放电模式 学习，在学习过程中的休息时间，以及 在学习后的测试中；3)基于个体 从模型的参数化生物学中产生的差异， 为了解释行为表现的个体差异 动物；以及4)证明(或改进，如果不成功) 通过预测小说中的行为结果来验证模型的可行性 训练情况。 计算机模拟将执行痕迹条件作用和 两种认知范式--传递性推理和横向推理 图案化。使用数学分析和简化的模型 海马体，它们的复杂性在系统上是不同的 和参数化时，申请人将尝试理解如何 典型的海马区解剖及相关生理学 复制归属于海马体的功能[包括 语境形成和灵活的记忆表征(Eichenbaum 等人，‘92)]以及为什么一些其他生物和参数 失败了。

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.