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.

中文翻译：

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计算认知神经科学教程：认知神经动力学建模

计算认知神经科学 (CCN) 是一个新领域，位于计算神经科学、机器学习和神经网络理论（即连接主义）的交叉点。理想的 CCN 模型不应做出与当前神经科学文献相矛盾的任何假设，同时提供对行为和至少一些神经科学数据（例如，单神经元活动、fMRI 数据）的良好解释。此外，一旦设置，CCN 网络的架构和每个单独单元的模型应在所有应用程序中保持固定。由于它们更重视生物准确性，CCN 模型在每个单独单元的建模方式、学习的建模方式以及网络生成行为的方式方面与传统的神经网络模型有很大不同。描述了针对这三个问题的各种 CCN 解决方案。描述了这种方法的一个真实示例，并讨论了 CCN 方法的一些优点和局限性。