Costi Leone, Hadjiivanov Alexander, Dold Dominik, Hale Zachary F, Izzo Dario
Advanced Concepts Team, European Space Research and Technology Centre, European Space Agency, 2201 AZ Noordwijk, The Netherlands.
Netherlands eScience Center, 1098 XH Amsterdam, The Netherlands.
Biomimetics (Basel). 2025 May 21;10(5):341. doi: 10.3390/biomimetics10050341.
In this work, we explore the possibility of using the topology and weight distribution of the connectome of a , or fruit fly, as a reservoir for multivariate chaotic time-series prediction. Based on the information taken from the recently released full connectome, we create the connectivity matrix of an Echo State Network. Then, we use only the most connected neurons and implement two possible selection criteria, either preserving or breaking the relative proportion of different neuron classes which are also included in the documented connectome, to obtain a computationally convenient reservoir. We then investigate the performance of such architectures and compare them to state-of-the-art reservoirs. The results show that the connectome-based architecture is significantly more resilient to overfitting compared to the standard implementation, particularly in cases already prone to overfitting. To further isolate the role of topology and synaptic weights, hybrid reservoirs with the connectome topology but random synaptic weights and the connectome weights but random topologies are included in the study, demonstrating that both factors play a role in the increased overfitting resilience. Finally, we perform an experiment where the entire connectome is used as a reservoir. Despite the much higher number of trained parameters, the reservoir remains resilient to overfitting and has a lower normalized error, under 2%, at lower regularisation, compared to all other reservoirs trained with higher regularisation.
在这项工作中，我们探索了将果蝇的连接体的拓扑结构和权重分布用作多变量混沌时间序列预测的储备池的可能性。基于从最近发布的完整连接体中获取的信息，我们创建了一个回声状态网络的连接矩阵。然后，我们仅使用连接性最强的神经元，并实施两种可能的选择标准，即保留或打破已记录的连接体中也包含的不同神经元类别的相对比例，以获得计算上方便的储备池。然后，我们研究这种架构的性能，并将它们与最先进的储备池进行比较。结果表明，与标准实现相比，基于连接体的架构对过拟合具有更强的弹性，特别是在已经容易出现过拟合的情况下。为了进一步分离拓扑结构和突触权重的作用，研究中纳入了具有连接体拓扑结构但突触权重随机以及具有连接体权重但拓扑结构随机的混合储备池，这表明这两个因素在提高过拟合弹性方面都发挥了作用。最后，我们进行了一项实验，将整个连接体用作储备池。尽管训练参数的数量要多得多，但与所有其他使用更高正则化训练的储备池相比，该储备池在较低正则化下仍能抵抗过拟合，并且归一化误差更低，低于2%。
