Khan Saad Ahmad, Thakore Vaibhav, Behal Aman, Bölöni Ladislau, Hickman James J
Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando FL, 32826, USA.
Department of Physics, University of Central Florida, Orlando FL, 32826, USA; Nanoscience Technology Center, University of Central Florida, Orlando FL, 32826, USA.
J Comput Theor Nanosci. 2013 Mar;10(3):573-580. doi: 10.1166/jctn.2013.2736.
Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction.
非侵入性神经电子接口在全细胞生物传感、生物计算和神经假体装置领域的应用，严重依赖于从神经元-电极连接处获取的信息的有效解码和处理。这就需要开发神经元-电极界面的数学模型，该模型要能真实地表示在神经电子连接处记录的细胞外信号，同时计算成本又不高。到目前为止，使用平面微电极或场效应晶体管阵列记录的细胞外信号，主要是用线性等效电路模型来表示的，而这些模型无法再现神经元-微电极界面记录的信号的正确幅度和形状。在本文中，为了探索对神经元-电极连接处进行计算成本低且高效建模的可行替代方法，使用参数维纳模型和具有外部输入的非线性自回归网络对神经元-电极连接处的输入-输出数据进行建模，该网络使用动态神经网络模型（NARX-NN模型）进行训练。然后，将这些模型对应验证数据集的结果用于比较和对比上述建模技术与用于估计神经电子连接处非线性动态模型的李-谢岑互相关技术的计算复杂度和效率。
