首页> 外文会议>Biosignals and Biorobotics Conference (BRC), 2012 ISSNIP >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
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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:一个基于Matlab的工具,用于通过大脑芯片接口从大鼠桶状皮层记录的局部场电势中的潜伏期计算来计算皮质层激活顺序的工具

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摘要

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,通过搅拌精确地执行对象定位,纹理和形状识别。相应桶形柱中的微电路被激活以隔离和整合在打扫过程中通过信息处理路径生成的触觉信息。当感觉信号传播时,皮层的不同层在不同的时间被激活,因此希望获得有关层激活顺序的精确信息,以更好地理解该途径。为了获得有关激活的精确定时信息,需要精确计算信号传播延迟。而且,可用的多位点和多通道神经元探针可以记录大量数据,这需要能够批量处理以确定皮质层激活顺序(CLAO)的自动化方法。在这项工作中,我们提出了一种自动且易于实现的方法,该方法可使用来自不同皮层深度的记录LFP的计算延迟和从LFP获得的“电流源密度”轮廓来确定CLAO。在对LFP进行广泛测试后,发现该方法是准确的,该LFP使用基于S1桶状皮质的基于电解质-氧化物-半导体场效应晶体管(EOSFET)的神经元探针记录。

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