School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China.
School of Automation and Electrical Engineering, Tianjin University of Technology and Educations, 300222, Tianjin, China.
Sci Rep. 2017 Jan 9;7:40152. doi: 10.1038/srep40152.
Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.
实时估计丘脑皮质细胞的动力学特性，如离子通道和膜电位的动力学，在帕金森状态下的丘脑研究中是有用且必不可少的。然而，从实验上测量离子通道的动力学特性极具挑战性，在临床应用中甚至是不可能的。本文提出并评估了一种用于丘脑皮质隐藏特性的实时估计系统。为了提高效率，我们使用现场可编程门阵列进行严格的基于硬件的计算和算法优化。在提出的系统中，基于 FPGA 的无迹卡尔曼滤波器被实现到基于电导的 TC 神经元模型中。由于 TC 神经元模型的复杂性限制了其在并行结构中的硬件实现，因此提出了一种具有成本效益的模型，在保留相关离子动力学的同时降低资源成本。实验结果表明，该系统在正常和帕金森状态下都具有高精度实时估计丘脑皮质隐藏特性的能力。虽然该方法被应用于估计丘脑的隐藏特性并探索帕金森状态的机制，但它也可以用于电生理实验的动态钳技术、神经控制工程和脑机接口研究。
