CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations
CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations
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发表时间：
2020-09
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通讯作者：
Bryan M. Li;Theoklitos Amvrosiadis;Nathalie L Rochefort;A. Onken
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作者：
Bryan M. Li;Theoklitos Amvrosiadis;Nathalie L Rochefort;A. Onken
钙离子成像已成为一种强大的和流行的技术，以监测大量的神经元在体内的活动。然而，出于道德考虑，尽管最近技术发展，记录仍然限于有限数量的试验和动物。这限制了从单个实验中获得的数据量，并阻碍了分析技术和模型的发展，以获得更真实的神经元群体大小。人工合成真实神经元钙信号的能力可以通过扩大试验数量来大大缓解这个问题。在这里，我们提出了一个生成对抗网络（GAN）模型，以生成逼真的钙信号，如在神经元胞体钙成像。为此，我们调整了WaveGAN架构，并使用Wasserstein距离对其进行训练。我们用已知的地面实况在人工数据上测试了该模型，并表明生成的信号的分布与底层数据分布非常相似。然后，我们在从行为小鼠的初级视觉皮层记录的真实的钙信号上训练模型，并确认去卷积的尖峰序列与记录数据的统计数据相匹配。总之，这些结果表明，我们的模型可以成功地生成逼真的钙成像数据，从而提供了增强现有神经元活动数据集的方法，以增强数据探索和建模。
Calcium imaging has become a powerful and popular technique to monitor the activity of large populations of neurons in vivo. However, for ethical considerations and despite recent technical developments, recordings are still constrained to a limited number of trials and animals. This limits the amount of data available from individual experiments and hinders the development of analysis techniques and models for more realistic size of neuronal populations. The ability to artificially synthesize realistic neuronal calcium signals could greatly alleviate this problem by scaling up the number of trials. Here we propose a Generative Adversarial Network (GAN) model to generate realistic calcium signals as seen in neuronal somata with calcium imaging. To this end, we adapt the WaveGAN architecture and train it with the Wasserstein distance. We test the model on artificial data with known ground-truth and show that the distribution of the generated signals closely resembles the underlying data distribution. Then, we train the model on real calcium signals recorded from the primary visual cortex of behaving mice and confirm that the deconvolved spike trains match the statistics of the recorded data. Together, these results demonstrate that our model can successfully generate realistic calcium imaging data, thereby providing the means to augment existing datasets of neuronal activity for enhanced data exploration and modeling.