Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling.

神经网络模型是提高我们对复杂大脑功能理解的潜在工具。为了实现这一目标，这些模型需要在神经生物学上具有现实性。然而，尽管神经网络近年来取得了巨大的进步，甚至在复杂的感知和认知任务上实现了类似人类的性能，但它们与大脑解剖学和生理学方面的相似性并不完美。在这里，我们讨论不同类型的神经模型，包括本地网络、自联想网络、异联想网络、深层网络和全脑网络，并确定可以改进其生物学合理性的方面。这些方面包括从模型神经元的选择、突触可塑性和学习机制的选择到抑制和控制的实施，以及神经解剖学特性，包括区域结构以及局部和远程连接。我们重点介绍了在发展以生物学为基础的认知理论方面的最新进展，以及在这些大脑受限的神经模型的基础上机械地解释迄今为止尚未解决的有关高级大脑功能的性质、定位以及个体发生和系统发育的问题。最后，我们指出了大脑约束建模未来可能的临床应用。