Hamilton Franz, Berry Tyrus, Peixoto Nathalia, Sauer Timothy
Electrical and Computer Engineering, George Mason University, Fairfax, Virginia 22030, USA.
Mathematical Sciences, George Mason University, Fairfax, Virginia 22030, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Nov;88(5):052715. doi: 10.1103/PhysRevE.88.052715. Epub 2013 Nov 21.
A nonlinear data assimilation technique is applied to determine and track effective connections between ensembles of cultured spinal cord neurons measured with multielectrode arrays. The method is statistical, depending only on confidence intervals, and requiring no form of arbitrary thresholding. In addition, the method updates connection strengths sequentially, enabling real-time tracking of nonstationary networks. The ensemble Kalman filter is used with a generic spiking neuron model to estimate connection strengths as well as other system parameters to deal with model mismatch. The method is validated on noisy synthetic data from Hodgkin-Huxley model neurons before being used to find network connections in the neural culture recordings.
一种非线性数据同化技术被应用于确定和追踪用多电极阵列测量的培养脊髓神经元集合之间的有效连接。该方法是统计性的，仅依赖于置信区间，且不需要任何形式的任意阈值设定。此外，该方法按顺序更新连接强度，能够实时追踪非平稳网络。集合卡尔曼滤波器与一个通用的发放神经元模型一起使用，以估计连接强度以及其他系统参数，从而处理模型不匹配问题。该方法在来自霍奇金-赫胥黎模型神经元的噪声合成数据上得到验证，然后用于在神经培养记录中寻找网络连接。
