论文标题
神经生物学过程的神经网络模型的观点和约束
Perspectives and constraints on neural network models of neurobiological processes
论文作者
论文摘要
人工和天然神经网络模型是一种新的工具包，可能已用于阐明复杂的大脑功能。要参加此目标，此类模型必须在神经生物学上现实。然而，尽管神经网络在近几十年来一直敏锐地发展，它们在大脑解剖学和生理方面的严格相似性并不完美。在这项工作中，我们讨论了不同类型的神经模型，包括本地主义，吸引者和深层网络模型，并确定可以提高其生物学信誉的方面。这些条件范围从神经元模型的选择以及突触可塑性的机制以及学习实施抑制和控制以及网络体系结构（模块化，连通性）。我们重点介绍了生物学启发的神经网络模型及其约束的最新进展。
Artificial and natural neural network models are a new toolkit which could be potentially have been used for clarifying of complex brain functions. To attend this goal, such models need to be neurobiologically realistic. However, although neural networks have advanced keenly in recent decades their strict similarity in aspects of brain anatomy and physiology is imperfect. In this work we discuss different types of neural models, including localist, attractor and deep network models, and also identify aspects under which their biological credibility can be improved. These conditions range from the choice of neuron models and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with network architectures (modularity, connectivity). We highlight recent advances in biologically inspired neural network models and their constraints.
