Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks
Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks
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发表时间：
2021-11
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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
了解任务学习后神经回路的活动如何重塑可以揭示学习的基本机制。由于神经成像技术的最新进展，可以在几天甚至几周内从数百个神经元获得高质量的记录。然而，人口反应的复杂性和维度给分析带来了重大挑战。现有的研究神经元适应和学习的方法常常对数据或模型强加强烈的假设，导致描述有偏差，无法概括。在这项工作中，我们使用一种称为 CycleGAN 的深度生成模型变体，来学习记录 $\textit{in vivo}$ 的学习前和学习后神经活动之间的未知映射。我们开发了一个端到端的管道来预处理、训练和评估钙荧光信号，以及一个解释生成的深度学习模型的程序。为了评估我们方法的有效性，我们首先在具有已知地面实况变换的合成数据集上测试我们的框架。随后，我们将我们的方法应用于从行为小鼠的初级视觉皮层记录的神经活动，其中小鼠在基于视觉的虚拟现实实验中从新手过渡到专家水平的表现。我们评估生成的钙信号及其推断的尖峰序列的模型性能。为了最大限度地提高性能，我们推导出一种新的方法来预排序神经元，以便基于卷积的网络可以利用神经活动中存在的空间信息。此外，我们采用视觉解释方法来提高我们工作的可解释性，并深入了解细胞活动中表现的学习过程。总之，我们的结果表明，使用数据驱动的深度无监督方法分析神经元学习过程有可能以公正的方式揭示变化。
Understanding how activity in neural circuits reshapes following task learning could reveal fundamental mechanisms of learning. Thanks to the recent advances in neural imaging technologies, high-quality recordings can be obtained from hundreds of neurons over multiple days or even weeks. However, the complexity and dimensionality of population responses pose significant challenges for analysis. Existing methods of studying neuronal adaptation and learning often impose strong assumptions on the data or model, resulting in biased descriptions that do not generalize. In this work, we use a variant of deep generative models called - CycleGAN, to learn the unknown mapping between pre- and post-learning neural activities recorded $\textit{in vivo}$. We develop an end-to-end pipeline to preprocess, train and evaluate calcium fluorescence signals, and a procedure to interpret the resulting deep learning models. To assess the validity of our method, we first test our framework on a synthetic dataset with known ground-truth transformation. Subsequently, we applied our method to neural activities recorded from the primary visual cortex of behaving mice, where the mice transition from novice to expert-level performance in a visual-based virtual reality experiment. We evaluate model performance on generated calcium signals and their inferred spike trains. To maximize performance, we derive a novel approach to pre-sort neurons such that convolutional-based networks can take advantage of the spatial information that exists in neural activities. In addition, we incorporate visual explanation methods to improve the interpretability of our work and gain insights into the learning process as manifested in the cellular activities. Together, our results demonstrate that analyzing neuronal learning processes with data-driven deep unsupervised methods holds the potential to unravel changes in an unbiased way.