Learning the dynamics of realistic models of C. elegans nervous system with recurrent neural networks.
Learning the dynamics of realistic models of C. elegans nervous system with recurrent neural networks.
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DOI：
10.1038/s41598-022-25421-w
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发表时间：
2023-01-10
影响因子：
4.6
通讯作者：
Silveira, Luis Miguel
中科院分区：
文献类型：
--
作者：
Barbulescu, Ruxandra;Mestre, Goncalo;Oliveira, Arlindo L.;Silveira, Luis Miguel
鉴于人类神经系统的继承复杂性，可以从研究较小和简单的生物中获得对大脑活动动力学的见解，而某些潜在的目标生物很简单，以至于它们的行为和结构生物学可能是众所周知的，并且可以理解，其他人可能仍会导致计算上的棘手模型，因为这些生物通常只是代理我们的理解力。在基本现象或功能上，通常对系统中每个神经元的详细演变不感兴趣，这足以观察神经元的子集，这些神经元捕获了神经元系统的深度非线性的效果在本文中，我们考虑了众所周知仅使用测量的输入信息，我们通常将其称为黑盒模型针对最新的经常性神经网络体系结构（例如短期内存和封闭式复发单元）的使用，并根据其属性及其属性比较这些体系结构精度（均方根误差）以及所得模型的复杂性。其输入的重要性以及对更多方案的可扩展性。
Given the inherent complexity of the human nervous system, insight into the dynamics of brain activity can be gained from studying smaller and simpler organisms. While some of the potential target organisms are simple enough that their behavioural and structural biology might be well-known and understood, others might still lead to computationally intractable models that require extensive resources to simulate. Since such organisms are frequently only acting as proxies to further our understanding of underlying phenomena or functionality, often one is not interested in the detailed evolution of every single neuron in the system. Instead, it is sufficient to observe the subset of neurons that capture the effect that the profound nonlinearities of the neuronal system have in response to different stimuli. In this paper, we consider the well-known nematode Caenorhabditis elegans and seek to investigate the possibility of generating lower complexity models that capture the system’s dynamics with low error using only measured or simulated input-output information. Such models are often termed black-box models. We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state-of-the-art recurrent neural network architectures such as Long Short-Term Memory and Gated Recurrent Units and compare these architectures in terms of their properties and their accuracy (Root Mean Square Error), as well as the complexity of the resulting models. We show that Gated Recurrent Unit models with a hidden layer size of 4 are able to accurately reproduce the system response to very different stimuli. We furthermore explore the relative importance of their inputs as well as scalability to more scenarios.
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DOI：
10.1098/rstb.2017.0380
发表时间：
2018-09-10
期刊：
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子：
--
作者：
Cantarelli M;Marin B;Quintana A;Earnshaw M;Court R;Gleeson P;Dura-Bernal S;Silver RA;Idili G
通讯作者：
Idili G
影响因子：
2.9
作者：
Gers, FA;Schmidhuber, J;Cummins, F
通讯作者：
Cummins, F
影响因子：
3
作者：
Brunton, Bingni W.;Johnson, Lise A.;Kutz, J. Nathan
通讯作者：
Kutz, J. Nathan
影响因子：
3.2
作者：
Kim, Jimin;Leahy, William;Shlizerman, Eli
通讯作者：
Shlizerman, Eli
影响因子：
2.5
作者：
ELMAN, JL
通讯作者：
ELMAN, JL