Atallah Hisham E, Frank Michael J, O'Reilly Randall C
Department of Psychology, Center for Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309, USA.
Neurobiol Learn Mem. 2004 Nov;82(3):253-67. doi: 10.1016/j.nlm.2004.06.004.
We present a framework for understanding how the hippocampus, neocortex, and basal ganglia work together to support cognitive and behavioral function in the mammalian brain. This framework is based on computational tradeoffs that arise in neural network models, where achieving one type of learning function requires very different parameters from those necessary to achieve another form of learning. For example, we dissociate the hippocampus from cortex with respect to general levels of activity, learning rate, and level of overlap between activation patterns. Similarly, the frontal cortex and associated basal ganglia system have important neural specializations not required of the posterior cortex system. Taken together, this overall cognitive architecture, which has been implemented in functioning computational models, provides a rich and often subtle means of explaining a wide range of behavioral and cognitive neuroscience data. Here, we summarize recent results in the domains of recognition memory, contextual fear conditioning, effects of basal ganglia lesions on stimulus-response and place learning, and flexible responding.
我们提出了一个框架，用于理解海马体、新皮层和基底神经节如何协同工作以支持哺乳动物大脑中的认知和行为功能。该框架基于神经网络模型中出现的计算权衡，即实现一种学习功能所需的参数与实现另一种学习形式所需的参数非常不同。例如，我们在活动的一般水平、学习率和激活模式之间的重叠程度方面将海马体与皮层区分开来。同样，前额叶皮层和相关的基底神经节系统具有后皮层系统不需要的重要神经特化。总体而言，这种已在运行的计算模型中实现的整体认知架构，提供了一种丰富且往往微妙的方式来解释广泛的行为和认知神经科学数据。在此，我们总结了识别记忆、情境恐惧条件反射、基底神经节损伤对刺激反应和位置学习的影响以及灵活反应等领域的最新研究结果。
