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首页> 外文期刊>Journal of Computational Neuroscience >Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
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Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity

机译：通过模拟尖峰活动重建数千个神经元的复发性突触连接

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摘要

Dynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal spiking activity. Here we present a method for the reconstruction of large recurrent neuronal networks from thousands of parallel spike train recordings. We employ maximum likelihood estimation of a generalized linear model of the spiking activity in continuous time. For this model the point process likelihood is concave, such that a global optimum of the parameters can be obtained by gradient ascent. Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities. We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters. For a simulated balanced random network of 1000 neurons, synaptic connectivity is recovered with a misclassification error rate of less than 1 % under ideal conditions. We show that the error rate remains low in a series of example cases under progressively less ideal conditions. Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups. Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.
机译：神经元网络的动力学和功能由其突触连接性决定。然而，当前在细胞水平上分析突触网络结构的实验方法仅覆盖了功能神经元回路的一小部分，通常没有同时记录神经元突刺活性。在这里，我们介绍一种从数千个并行峰值火车记录中重建大型循环神经网络的方法。我们在连续时间内采用峰值活动的广义线性模型的最大似然估计。对于该模型，点过程的似然性是凹入的，因此可以通过梯度上升获得参数的全局最优值。先前的方法，包括同类方法，由于计算成本过高和数值不稳定，因此无法重建该数量级的递归网络。我们描述了针对大型网络进行优化的最小模型，以及针对通用计算集群进行并行数值优化的有效方案。对于具有1000个神经元的模拟平衡随机网络，在理想条件下以小于1％的误分类错误率恢复了突触连接。我们表明，在逐渐变得不太理想的条件下，一系列示例中的错误率仍然很低。最后，我们成功地重构了嵌入在随机网络中的隐藏synfire链的连通性，这需要对网络连通性进行聚类以揭示synfire组。我们的结果表明，如何从大规模平行峰信号记录中推断出突触连接性。

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