使用生成对抗网络捕获生物调谐曲线的多样性
Capturing the diversity of biological tuning curves using generative adversarial networks
摘要：
表征生物神经元的响应选择性的调谐曲线通常在神经元之间表现出很大程度的不规则性和多样性。具有异质细胞群或随机连接性的理论网络模型也会产生不同的调谐曲线。然而，一个通用的框架来拟合这样的模型，以实验测量的调谐曲线是缺乏的。我们解决这个问题，建议将机械网络模型视为生成模型，其参数可以优化，以适应实验测量的调谐曲线的分布。拟合这种模型的一个主要障碍是，它们的似然函数不是显式可用的，或者是非常难以计算的。机器学习的最新进展提供了用于拟合生成模型而无需评估似然性及其梯度的方法。生成对抗网络（GAN）提供了一个这样的框架，它在传统的机器学习任务中取得了成功。我们在两个独立的实验中应用这种方法，展示了如何使用GANs将理论神经科学中常用的机械模型拟合到测量的调谐曲线数据集。该拟合过程避免了推断潜在变量（例如，个体细胞的生物物理参数或完整突触连接性矩阵的特定实现）的计算上昂贵的步骤，并且直接学习表征连接性或单细胞性质的统计的模型参数。这种方法的另一个优点是，它适合实验调谐曲线的整个联合分布，而不是匹配用户先验选择的一些汇总统计数据。更一般地说，这个框架打开了大门，以拟合理论激励的动态网络模型直接同时或非同时记录的神经反应。
英文摘要：
Tuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable to compute. Recent advances in machine learning provide ways for fitting generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GAN) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic models in theoretical neuroscience to datasets of measured tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, e.g. the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or of single-cell properties. Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked a priori by the user. More generally, this framework opens the door to fitting theoretically motivated dynamical network models directly to simultaneously or non-simultaneously recorded neural responses.
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