Neural architectures and their connectivity

其中一个重点是有效的连接算法，以及如何将它们应用于理解人类的大脑功能。一个目的是测量有效连接如何随着不同的任务需求而变化，以研究大脑中的信息流。为了研究神经结构，一种方法是利用遗传算法为定义的计算任务创建生物学上合理的结构。相关路线将研究网络设计如何与输入中的不变性相关，例如使用记忆跟踪学习规则或深度学习的几何方法。了解环境统计和神经网络设计之间的关系可能会导致在无监督学习和神经结构搜索领域的实际应用。最后，关于大脑连接和结构的知识不仅对计算神经科学很重要，而且可能导致在医学科学中的应用。

One focus is on effective connectivity algorithms and how they can be applied to understanding human brain function. An aim is to measure how effective connectivity changes with different task demands to study information flow in the brain. To investigate neural architectures, one route will utilise genetic algorithms to create biologically plausible architectures for defined computational tasks. A related route will investigate how the network design relates to the invariances in the input, using for example memory trace learning rules or geometric approaches to deep learning. Understanding the relationship between environmental statistics and neural network design may lead real world applications in the fields of unsupervised learning and neural architecture search. Finally, knowledge of both the connectivity and architecture of the brain is not only important for computational neuroscience, but may lead to applications in medical science.