Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.
Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.
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DOI：
10.1371/journal.pcbi.1004464
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发表时间：
2015-10
影响因子：
4.3
通讯作者：
Paninski L
中科院分区：
文献类型：
--
作者：
Soudry D;Keshri S;Stinson P;Oh MH;Iyengar G;Paninski L
推断神经网络的连通性仍然是统计神经科学的一个关键挑战。“共同输入”问题提出了一个主要的障碍：很难可靠地区分观察到的神经元对之间的因果关系，以及由未观察到的神经元的共同输入引起的相关性。现有的技术允许我们以足够的时间分辨率同时记录网络的一小部分。因此，忽略这些常见输入效应的朴素连接估计器是高度偏倚的。这项工作提出了一个“霰弹枪”实验设计，在这个设计中，我们以串行的方式简要地观察了多个子网络。因此，虽然不能在任何给定时间同时观察到整个网络，但我们可以在整个实验过程中观察到更大的网络子集，从而改善了常见的输入问题。使用尖峰递归神经网络的广义线性模型，我们开发了一种可扩展的基于近似期望对数似然的贝叶斯方法来执行网络推理，给定这种类型的数据，其中在每个时间bin中仅观察到网络的一小部分。仿真结果表明，霰弹枪实验设计可以消除由共同输入效应引起的偏差。拥有数千个神经元的网络，在每个时间库中只观察到一小部分神经元，可以快速准确地估计，比以前的方法速度提高了几个数量级。神经网络活动的光学成像受到成像设备扫描速度的限制。因此，在整个实验过程中，通常只观察到一小部分固定的网络。然而，在这样的实验中，很难从观察到的活动模式中推断出：(1)神经元a直接影响神经元B，还是(2)另一个未观察到的神经元C同时影响a和B。为了解决这个问题，我们提出了一种“散弹枪”观察方案，在每个时间点，我们观察网络中神经元的一个小变化子集。因此，在整个实验过程中，很少有神经元完全未被观察到，这使我们能够在足够长的实验时间内最终区分情况(1)和(2)。由于以前的推理算法不能有效地处理如此多的缺失观测值，我们开发了一种可扩展的算法，用于使用霰弹枪观测方案获取的数据，其中每个时间bin中只有一小部分神经元被观察到。利用这类模拟数据，我们证明了该算法能够快速推断具有数千个神经元的尖峰循环网络的连通性。
Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The “common input” problem presents a major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons versus correlations induced by common input from unobserved neurons. Available techniques allow us to simultaneously record, with sufficient temporal resolution, only a small fraction of the network. Consequently, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a “shotgun” experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe much larger subsets of the network over the course of the entire experiment, thus ameliorating the common input problem. Using a generalized linear model for a spiking recurrent neural network, we develop a scalable approximate expected loglikelihood-based Bayesian method to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that the shotgun experimental design can eliminate the biases induced by common input effects. Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, can be quickly and accurately estimated, achieving orders of magnitude speed up over previous approaches. Optical imaging of the activity in a neuronal network is limited by the scanning speed of the imaging device. Therefore, typically, only a small fixed part of the network is observed during the entire experiment. However, in such an experiment, it can be hard to infer from the observed activity patterns whether (1) a neuron A directly affects neuron B, or (2) another, unobserved neuron C affects both A and B. To deal with this issue, we propose a “shotgun” observation scheme, in which, at each time point, we observe a small changing subset of the neurons from the network. Consequently, many fewer neurons remain completely unobserved during the entire experiment, enabling us to eventually distinguish between cases (1) and (2) given sufficiently long experiments. Since previous inference algorithms cannot efficiently handle so many missing observations, we develop a scalable algorithm for data acquired using the shotgun observation scheme, in which only a small fraction of the neurons are observed in each time bin. Using this kind of simulated data, we show the algorithm is able to quickly infer connectivity in spiking recurrent networks with thousands of neurons.