Multifunctionality describes the capacity for a neural network to perform multiple mutually exclusive tasks without altering its network connections; and is an emerging area of interest in the reservoir computing machine learning paradigm. Multifunctionality has been observed in the brains of humans and other animals: particularly, in the lateral horn of the fruit fly. In this work, we transplant the connectome of the fruit fly lateral horn to a reservoir computer (RC), and investigate the extent to which this 'fruit fly RC' (FFRC) exhibits multifunctionality using the 'seeing double' problem as a benchmark test. We furthermore explore the dynamics of how this FFRC achieves multifunctionality while varying the network's spectral radius. Compared to the widely-used Erd\"os-Renyi Reservoir Computer (ERRC), we report that the FFRC exhibits a greater capacity for multifunctionality; is multifunctional across a broader hyperparameter range; and solves the seeing double problem far beyond the previously observed spectral radius limit, wherein the ERRC's dynamics become chaotic.

翻译：多功能性描述的是神经网络在不改变网络连接的情况下执行多个互斥任务的能力，并且是储层计算机器学习范式中一个新兴的研究领域。在人类及其他动物的大脑中——特别是果蝇的侧角——已观察到多功能性。在本研究中，我们将果蝇侧角的连接组移植到储层计算机中，并以“双重视觉”问题为基准测试，探究这种“果蝇储层计算机”展现多功能性的程度。此外，我们进一步研究了该果蝇储层计算机在改变网络谱半径时实现多功能性的动力学机制。与广泛使用的埃尔德什-雷尼储层计算机相比，我们报告称：果蝇储层计算机展现出更强的多功能性；能在更宽的超参数范围内实现多功能；并能远超此前观察到的谱半径极限（在此极限下，埃尔德什-雷尼储层计算机的动力学变得混沌）解决“双重视觉”问题。