In this paper, we develop methods for efficient and accurate information extraction from calcium-imaging-based neural signals. The particular form of information extraction we investigate involves predicting behavior variables linked to animals from which the calcium imaging signals are acquired. More specifically, we develop algorithms to systematically generate compact deep neural network (DNN) models for accurate and efficient calcium-imaging-based predictive modeling. We also develop a software tool, called NeuroGRS, to apply the proposed methods for compact DNN derivation with a high degree of automation. GRS stands for Greedy inter-layer order with Random Selection of intra-layer units, which describes the central algorithm developed in this work for deriving compact DNN structures. Through extensive experiments using NeuroGRS and calcium imaging data, we demonstrate that our methods enable highly streamlined information extraction from calcium images of the brain with minimal loss in accuracy compared to much more computationally expensive approaches.

在本文中，我们开发了从基于钙成像的神经信号中高效，准确地提取信息的方法。我们研究的信息提取的特定形式涉及预测与动物相关的行为变量，从中获取钙成像信号。更具体地说，我们开发算法来系统地生成紧凑的深度神经网络（DNN）模型，以进行准确，高效的基于钙成像的预测建模。我们还开发了一种名为NeuroGRS的软件工具，以将建议的方法应用于高度自动化的紧凑型DNN派生。GRS代表层间单位随机选择的贪婪层间顺序，它描述了在这项工作中开发的用于导出紧凑DNN结构的中央算法。