Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many benefits from a machine learning perspective. Given the association to multistability, in this paper we explore how the relationship between different attractors influences the ability of a reservoir computer (RC), which is a dynamical system in the form of an ANN, to achieve multifunctionality. We construct the `seeing double' problem to systematically study how a RC reconstructs a coexistence of attractors when there is an overlap between them. As the amount of overlap increases, we discover that for multifunctionality to occur, there is a critical dependence on a suitable choice of the spectral radius for the RC's internal network connections. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime that can lead to chaotic itinerancy.

翻译：多功能生物神经网络利用多稳态特性，在不改变网络属性的前提下执行多项任务。从机器学习角度看，使人工神经网络获得特定多稳态能力以执行多项任务（每个任务对应网络状态空间中的特定吸引子）自然具有诸多益处。基于多稳态的关联性，本文探究不同吸引子之间的关系如何影响储层计算网络（一种以人工神经网络形式存在的动力系统）实现多功能性的能力。我们构建了“看见双重”问题，以系统研究储层计算网络在吸引子存在重叠时如何重构吸引子共存的机制。随着重叠程度的增加，我们发现实现多功能性对储层计算网络内部网络连接谱半径的恰当选择存在关键依赖性。分岔分析揭示了多功能性如何产生与消失——当储层计算网络进入混沌状态时（可能引发混沌巡游），原本的稳定多态结构会遭到破坏。