Dynamical segmentation of single trials from population neural data
Dynamical segmentation of single trials from population neural data
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发表时间：
2011-12
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通讯作者：
B. Petreska;Byron M. Yu;J. Cunningham;G. Santhanam;S. Ryu;K. Shenoy;M. Sahani
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作者：
B. Petreska;Byron M. Yu;J. Cunningham;G. Santhanam;S. Ryu;K. Shenoy;M. Sahani
同时记录嵌入在一个反复连接的皮层网络中的许多神经元，可以同时观察该网络的动力学过程，从而了解其计算功能。原则上，这些动态可以通过纯粹无监督的统计手段来识别。在这里，我们表明，隐藏开关线性动力系统（HSLDS）模型-其中多个线性动力学定律近似一个非线性和潜在的非平稳动力学过程-是能够区分不同的动力学制度内的单次试验运动皮层活动与手的运动的准备和启动。这些制度的确定没有参考行为或实验时代，但它们之间的过渡强烈相关的外部事件的时间可能会有所不同，从审判。HSLDS模型在基于其他神经元的放电率预测孤立神经元的放电率方面也比最近的可比模型表现得更好，这表明它捕获了更多的数据“共享方差”。因此，该方法是能够跟踪的动力学过程的协调演变的网络活动的方式，似乎反映其计算的作用。
Simultaneous recordings of many neurons embedded within a recurrently-connected cortical network may provide concurrent views into the dynamical processes of that network, and thus its computational function. In principle, these dynamics might be identified by purely unsupervised, statistical means. Here, we show that a Hidden Switching Linear Dynamical Systems (HSLDS) model— in which multiple linear dynamical laws approximate a nonlinear and potentially non-stationary dynamical process—is able to distinguish different dynamical regimes within single-trial motor cortical activity associated with the preparation and initiation of hand movements. The regimes are identified without reference to behavioural or experimental epochs, but nonetheless transitions between them correlate strongly with external events whose timing may vary from trial to trial. The HSLDS model also performs better than recent comparable models in predicting the firing rate of an isolated neuron based on the firing rates of others, suggesting that it captures more of the "shared variance" of the data. Thus, the method is able to trace the dynamical processes underlying the coordinated evolution of network activity in a way that appears to reflect its computational role.