Ashby F Gregory, Helie Sebastien
University of California, Santa Barbara.
J Math Psychol. 2011 Aug 1;55(4):273-289. doi: 10.1016/j.jmp.2011.04.003.
Computational Cognitive Neuroscience (CCN) is a new field that lies at the intersection of computational neuroscience, machine learning, and neural network theory (i.e., connectionism). The ideal CCN model should not make any assumptions that are known to contradict the current neuroscience literature and at the same time provide good accounts of behavior and at least some neuroscience data (e.g., single-neuron activity, fMRI data). Furthermore, once set, the architecture of the CCN network and the models of each individual unit should remain fixed throughout all applications. Because of the greater weight they place on biological accuracy, CCN models differ substantially from traditional neural network models in how each individual unit is modeled, how learning is modeled, and how behavior is generated from the network. A variety of CCN solutions to these three problems are described. A real example of this approach is described, and some advantages and limitations of the CCN approach are discussed.
计算认知神经科学（CCN）是一个新兴领域，它处于计算神经科学、机器学习和神经网络理论（即联结主义）的交叉点。理想的CCN模型不应做出任何已知与当前神经科学文献相矛盾的假设，同时要能很好地解释行为并至少能解释一些神经科学数据（例如单神经元活动、功能磁共振成像数据）。此外，一旦设定，CCN网络的架构和每个单独单元的模型在所有应用中都应保持固定。由于更注重生物学准确性，CCN模型在每个单独单元的建模方式、学习的建模方式以及如何从网络生成行为方面与传统神经网络模型有很大不同。本文描述了针对这三个问题的多种CCN解决方案。文中还描述了这种方法的一个实际例子，并讨论了CCN方法的一些优点和局限性。
