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.

通过将果蝇侧角 connectome 移植到储水池计算机中，研究储水池计算机中是否存在多功能性，并使用“双倍看见”问题作为基准测试，研究如何实现多功能性。结果表明，与Erd"os-Renyi Reservoir Computer相比，该储水池计算机具有更大的多功能性能力，可以解决以前观察到的光谱半径极限之外的问题。

基于连接组成的水库计算机的多功能性