论文标题
在回声状态网络上强加了连接派的拓扑
Imposing Connectome-Derived Topology on an Echo State Network
论文作者
论文摘要
连接衍生的约束可以为计算提供信息吗？在本文中，我们调查了果蝇连接组对回声状态网络（ESN）的性能的贡献 - 储层计算的子集，这是混乱的时间序列预测中的最新水平。具体而言，我们替换了经典ESN的储层层 - 通常是固定的，随机图为2-D矩阵 - 具有特定的（雌性）果蝇连接的连接矩阵。我们将这种实验类型的模型（带有连接组来源的储层）称为“果蝇ESN”（FFESNS）。我们在混乱的时间序列预测任务中训练和验证FFESN；在这里，我们考虑了四组试验，这些试验具有不同的训练输入尺寸（小，大）和火车估算的拆分（两个变体）。我们将所有最佳FFESN模型的验证性能（于点误差）与一类控制模型ESN（简单地称为“ ESN”）进行了比较。总体而言，对于所有四组试验，我们发现FFESN要么明显优于ESN（差异较低）。或仅具有比ESN较低的差异。
Can connectome-derived constraints inform computation? In this paper we investigate the contribution of a fruit fly connectome's topology on the performance of an Echo State Network (ESN) -- a subset of Reservoir Computing which is state of the art in chaotic time series prediction. Specifically, we replace the reservoir layer of a classical ESN -- normally a fixed, random graph represented as a 2-d matrix -- with a particular (female) fruit fly connectome-derived connectivity matrix. We refer to this experimental class of models (with connectome-derived reservoirs) as "Fruit Fly ESNs" (FFESNs). We train and validate the FFESN on a chaotic time series prediction task; here we consider four sets of trials with different training input sizes (small, large) and train-validate splits (two variants). We compare the validation performance (Mean-Squared Error) of all of the best FFESN models to a class of control model ESNs (simply referred to as "ESNs"). Overall, for all four sets of trials we find that the FFESN either significantly outperforms (and has lower variance than) the ESN; or simply has lower variance than the ESN.
