We report an improvement to the conventional Echo State Network (ESN) across three benchmark chaotic time-series prediction tasks using fruit fly connectome data alone. We also investigate the impact of key connectome-derived structural features on prediction performance -- uniquely bridging neurobiological structure and machine learning function; and find that both increasing the global average clustering coefficient and modifying the position of weights -- by permuting their synapse-synapse partners -- can lead to increased model variance and (in some cases) degraded performance. In all we consider four topological point modifications to a connectome-derived ESN reservoir (null model): namely, we alter the network sparsity, re-draw nonzero weights from a uniform distribution, permute nonzero weight positions, and increase the network global average clustering coefficient. We compare the four resulting ESN model classes -- and the null model -- with a conventional ESN by conducting time-series prediction experiments on size-variants of the Mackey-Glass 17 (MG-17), Lorenz, and Rossler chaotic time series; denoting each model's performance and variance across train-validate trials.

翻译：我们报告了在三个基准混沌时间序列预测任务中，仅利用果蝇连接组数据对传统回声状态网络（ESN）进行的改进。我们还研究了关键连接组衍生结构特征对预测性能的影响——独特地架起了神经生物学结构与机器学习功能之间的桥梁；发现同时提高全局平均聚类系数和通过置换突触-突触配对来修改权重位置，可能导致模型方差增大，并在某些情况下降低性能。总体而言，我们考虑了四种对连接组衍生ESN储备池（零模型）的拓扑点修改：具体包括改变网络稀疏度、从均匀分布重新抽取非零权重、置换非零权重位置，以及提高网络全局平均聚类系数。我们通过在不同规模的Mackey-Glass 17（MG-17）、Lorenz和Rossler混沌时间序列上开展预测实验，将四种改进的ESN模型类别及零模型与传统ESN进行比较，并记录了每个模型在训练-验证试验中的性能及方差。