xgboost代码回归matlab-neural_decoding:解码来自KordingLab的分析代码
xgboost代码回归matlab
神经解码：
包含许多用于解码神经活动的方法的python软件包
该软件包包含经典解码方法（维纳滤波器，维纳级联，卡尔曼滤波器，支持向量回归）和现代机器学习方法（XGBoost，密集神经网络，递归神经网络，GRU，LSTM）的混合。
当前设计解码器来预测连续值的输出。
将来，我们将修改功能以允许分类。
该程序包随附一个，用于比较这些方法在多个数据集上的性能。
如果您在研究中使用我们的代码，请引用该手稿，我们将不胜感激。
依存关系
为了运行所有基于神经网络的解码器，您需要安装为了运行XGBoost解码器，您需要安装为了运行维纳滤波器，维纳级联或支持向量回归，您将需要。
入门
我们提供了jupyter笔记本，其中提供了有关如何使用解码器的详细示例。
文件“
Examples_kf_decoder”用于卡尔曼滤波器解码器，文件“
Examples_all_decoders”用于所有其他解码器。
在这里，我们提供一个使用LSTM解码器的基本示例。
对于此示例，我们假设我们已经加载了矩阵：
“
neural_data”：大小为“时间段总数”
x“神经元数量”的矩
文件下载
资源详情
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