Rodriguez Paul, Levy William B
Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Box 951563 Los Angeles, CA 90095-1563, USA.
Neural Netw. 2004 Mar;17(2):175-90. doi: 10.1016/j.neunet.2003.06.001.
The hippocampus is necessary in both humans and rats for learning configural representations in tasks such as transverse patterning. The transverse patterning task, (A+B-, B+C-, C+A-), requires representing individual stimuli in the context of other stimuli. This paper extends a previous application to rat data [INNS World Congress on Neural Networks, 1995; Biol Cybern 6 (1998a) 203] by applying a model of the CA3 region of the hippocampus to human data. A decision function is also added that enables the system to choose among training items. Analysis of the simulations show that configural representations are formed by unique neural codes that depend on temporal and stimuli context. Based on the simulations, we hypothesize that configural representations in biological networks depend on a proper balance of input and context representations. Furthermore, the division of labor between functions in the model is a specific working hypothesis that in learning this task the hippocampus specializes in sequence prediction and the decision function evaluates the predictions.
海马体对于人类和大鼠在诸如横向模式化等任务中学习构型表征都是必需的。横向模式化任务（A+B-，B+C-，C+A-）要求在其他刺激的背景下表征单个刺激。本文通过将海马体CA3区域的模型应用于人类数据，扩展了先前对大鼠数据的应用[国际神经网络大会，1995年；生物控制论6（1998a）203]。还添加了一个决策函数，使系统能够在训练项目中进行选择。模拟分析表明，构型表征是由依赖于时间和刺激背景的独特神经编码形成的。基于这些模拟，我们假设生物网络中的构型表征依赖于输入和背景表征的适当平衡。此外，模型中功能之间的分工是一个特定的工作假设，即在学习此任务时，海马体专门负责序列预测，而决策函数则评估这些预测。
