论文标题
使用Connectome功能来限制ECHO状态网络
Using Connectome Features to Constrain Echo State Networks
论文作者
论文摘要
我们仅使用果蝇连接组数据来报告三个基准混沌时间序列的预测任务对传统回声状态网络（ESN）的改进。我们还研究了关键连接组的结构特征对预测性能的影响 - 独特的神经生物学结构和机器学习功能；并发现同时增加全球平均聚类系数和修改权重的位置（通过将其突触突触合作伙伴定位）都可以导致模型差异增加，并且（在某些情况下）降低了性能。我们总而言之，我们考虑了对连接组来源的ESN储层（无效模型）的四个拓扑点修改：即，我们改变网络稀疏性，从均匀分布中重新分配非零权重，取消非零重量位置，并增加网络全球平均群集系数。我们通过对Mackey-Glass 17（MG-17），Lorenz和Rossler Chaotic Chaotic Chaotic Time序列进行时间序列预测实验，将所得的四个ESN模型类别和NULL模型与常规ESN进行比较。在火车估算试验中表示每个模型的性能和差异。
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.
