Department of Electrical Engineering and Computer Sciences, Massachusetts Institute of Technology, Cambridge MA 02139, USA.
IEEE Trans Biomed Eng. 2012 Jul;59(7):2030-9. doi: 10.1109/TBME.2012.2196699. Epub 2012 Apr 26.
An extraordinary amount of electrophysiological data has been collected from various brain nuclei to help us understand how neural activity in one region influences another region. In this paper, we exploit the point process modeling (PPM) framework and describe a method for constructing aggregate input-output (IO) stochastic models that predict spiking activity of a population of neurons in the "output" region as a function of the spiking activity of a population of neurons in the "input" region. We first build PPMs of each output neuron as a function of all input neurons, and then cluster the output neurons using the model parameters. Output neurons that lie within the same cluster have the same functional dependence on the input neurons. We first applied our method to simulated data, and successfully uncovered the predetermined relationship between the two regions. We then applied our method to experimental data to understand the input-output relationship between motor cortical neurons and 1) somatosensory and 2) premotor cortical neurons during a behavioral task. Our aggregate IO models highlighted interesting physiological dependences including relative effects of inhibition/excitation from input neurons and extrinsic factors on output neurons.
已经从各种脑核中收集了大量的电生理数据，以帮助我们了解一个区域的神经活动如何影响另一个区域。在本文中，我们利用点过程建模 (PPM) 框架，描述了一种构建聚合输入-输出 (IO) 随机模型的方法，该模型可以预测“输出”区域中神经元群体的尖峰活动作为“输入”区域中神经元群体的尖峰活动的函数。我们首先构建每个输出神经元作为所有输入神经元的函数的 PPM，然后使用模型参数对输出神经元进行聚类。位于同一聚类中的输出神经元对输入神经元具有相同的功能依赖性。我们首先将我们的方法应用于模拟数据，并成功揭示了这两个区域之间的预定关系。然后，我们将我们的方法应用于实验数据，以了解运动皮层神经元与 1）躯体感觉和 2）运动前皮层神经元在行为任务期间的输入-输出关系。我们的聚合 IO 模型突出了有趣的生理依赖性，包括来自输入神经元的抑制/兴奋和外部因素对输出神经元的相对影响。
