论文标题
储层计算机中的多功能性
Multifunctionality in a Reservoir Computer
论文作者
论文摘要
多功能性是生物神经网络的现象学特征，被认为对某些物种随时间的生存至关重要。这些多功能神经网络能够执行多个任务而无需更改任何网络连接。在本文中，我们研究了如何在人工环境中使用现代机器学习范式（称为“储层计算”）实现这种神经系统特质。培训技术旨在使储层计算机能够执行多功能性质的任务。我们探讨了某些参数变化可以对储层计算机表达多功能的能力的关键效果。我们还揭示了几个“未经训练的吸引者”的存在；居住在储层计算机的预测状态空间中的吸引子，而不是培训的一部分。我们对这些未经训练的吸引子进行分叉分析，并讨论结果的含义。
Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over time. These multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we investigate how this neurological idiosyncrasy can be achieved in an artificial setting with a modern machine learning paradigm known as `Reservoir Computing'. A training technique is designed to enable a Reservoir Computer to perform tasks of a multifunctional nature. We explore the critical effects that changes in certain parameters can have on the Reservoir Computers' ability to express multifunctionality. We also expose the existence of several `untrained attractors'; attractors which dwell within the prediction state space of the Reservoir Computer that were not part of the training. We conduct a bifurcation analysis of these untrained attractors and discuss the implications of our results.
