Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

被引:11
|
作者
Wang, Paul Y. [1 ,2 ]
Sapra, Sandalika [1 ,3 ]
George, Vivek Kurien [1 ,4 ]
Silva, Gabriel A. [1 ,4 ,5 ]
机构
[1] Univ Calif San Diego, Ctr Engn Nat Intelligence, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Dept Phys, La Jolla, CA 92093 USA
[3] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
[4] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA
[5] Univ Calif San Diego, Dept Neurosci, La Jolla, CA 92093 USA
来源
关键词
calcium imaging; graph neural network; deep learning; C elegans; motor action classification;
D O I
10.3389/frai.2021.618372
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.
引用
收藏
页数:11
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