Biologically inspired adaptive mechanisms for artificial neural networks

人工神经网络的受生物学启发的自适应机制

• 批准号: 2504973
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2504973
• 关键词: Biologically; inspired; adaptive; mechanisms; artificial

Relational reasoning is a crucial cognitive capacity of humans and other non-human animals and it can be viewed as an generalized concept of a task rule that can be applied whenever certain relations between objects hold true. Animals and humans readily learn statistical regularities in their environment, which is demonstrated by their ability to quickly apply the same task structure in different settings as shown by reversal learning, among other findings. It is therefore possible that relational reasoning and generalization are facilitated by similar architectural characteristics. It has been shown that varying levels of neuron selectivity and firing rates influence the ability to retain contextual information in animals, which enables making different actions depending on the task at hand. However, the optimal neural connections are formed not just by learning task related aspects and so strengthening connections between different units, but also by degrading connections that are unnecessary and may interfere with future learning. This is often an issue in artificial neural networks, where novel examples can interfere with previously learned rules. The aim of this project is to implement mechanisms that are analogous to modulatory systems that facilitate learning and forgetting in biological nervous systems and investigate the effect of such systems on lifelong learning in artificial neural networks.

关系推理是人类和其他非人类动物的一种重要的认知能力，它可以被视为一种任务规则的广义概念，可以在对象之间的特定关系成立时应用。动物和人类很容易在他们的环境中学习统计规律，这体现在他们在不同的环境中快速应用相同的任务结构的能力，正如逆转学习等发现所示。因此，相似的体系结构特征可能促进关系推理和泛化。已有研究表明，不同水平的神经元选择性和放电频率会影响动物保留上下文信息的能力，这使得根据手头的任务做出不同的动作。然而，最佳的神经连接不仅是通过学习与任务相关的方面来形成的，因此加强了不同单位之间的连接，而且还通过降低不必要的连接来形成，这些连接可能会干扰未来的学习。这在人工神经网络中经常是一个问题，在人工神经网络中，新的例子可能会干扰以前学习的规则。该项目的目的是实现类似于生物神经系统中促进学习和遗忘的调节系统的机制，并研究这种系统对人工神经网络中终身学习的影响。