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A Matlab based tool for cortical layer activation order detection through latency calculation in local field potentials recorded from rat barrel cortex by brain-chip interface

机译:一种基于MATLAB基于脑芯片接口从大鼠桶皮层记录的局部场电位延迟计算的皮质层激活订单检测工具

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Rodents explore the environment, perform object localization, texture and shape discriminations precisely through whisking. Microcircuits in the corresponding barrel columns get activated to segregate and integrate the tactile information generated during whisking through the information processing pathway. While the sensory signals propagate, different layers of the cortex get activated at different times, thus having precise information about the order of layer activation is desired to better understand this pathway. To have precise timing information about the activations, accurate calculation of signal propagation latencies is required. Moreover, available multisite and multichannel neuronal probes can record a huge amount of data which require an automated method capable of batch processing to determine the cortical layer activation order (CLAO). In this work we propose an automated and easy to implement method to determine the CLAO using calculated latencies from the recorded LFPs at different cortical depths and the Current Source Density profile obtained from the LFPs. The method is found accurate after performing extensive tests on LFPs recorded using Electrolyte-Oxide-Semiconductor Field Effect Transistor (EOSFET) based neuronal probes from S1 barrel cortex.
机译:啮齿动物探索环境,精确地通过搅拌来执行对象本地化,纹理和形状鉴别。相应的桶列中的微电路被激活以分离并整合通过信息处理路径搅拌期间产生的触觉信息。虽然感觉信号传播,但在不同时间激活皮质的不同层,因此希望能够更好地理解该路径的关于层激活的顺序的精确信息。为了具有关于激活的精确定时信息,需要精确计算信号传播延迟。此外,可用的多路和多通道神经元探针可以记录大量数据,该数据需要一种能够进行批量处理的自动化方法以确定皮质层激活顺序(Clao)。在这项工作中,我们提出了一种自动且易于实现的方法,以确定使用从不同皮质深度的记录的LFP的计算的延迟和从LFP获得的电流源密度分布的延迟来确定Clao。在使用来自S1桶皮层的基于电解质氧化物半导体场效应晶体管(EOSFET)的神经元探针在记录的LFP上进行广泛的测试后,可以确定该方法。

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