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.

该研究探讨了多功能生物神经网络如何利用多稳定性执行多个任务，并研究了如何在人工神经网络中实现多稳定性以实现多个任务的性能，其中每个任务都与网络状态空间中的特定吸引子相关。该研究提出了 “看双重” 问题，并发现多功能性存在临界依赖于适当的内部网络连接的谱半径选择。通过分叉分析，研究表明在 RC 进入导致混沌漫游的混沌区域时，多功能性如何出现和破坏。