大鼠初级视觉皮层局部菲尔电位的运动选择性:机器学习方法

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abbas Pourhedayat, Marzie Aghababaeipour Dehkordi, Mohammad Reza Daliri
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引用次数: 0

摘要

使用啮齿动物作为生理视觉研究的模型,需要充分了解其视觉皮层的信息。虽然大鼠的初级视觉皮层有不同的亚区,但有关这些亚区不同反应模式的研究却很少。在这项研究中,我们记录了麻醉大鼠初级视皮层(V1)亚区的局部场电位(LFPs)。我们使用随机点图案作为移动刺激,以随机序列呈现。然后,我们使用机器学习方法从记录的信号中解码刺激的方向和速度。我们的研究结果表明,不同亚区域对运动刺激的反应模式是不同的。虽然使用 LFPs 的解码结果并不高,但当移动到 V1 的外侧子区域时,解码结果会得到增强。我们的结果表明,记录区域的位置会影响反应时间、时域和频域的反应模式以及对运动刺激的编码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Motion Selectivity of the Local Filed Potentials in the Primary Visual Cortex of Rats: A Machine Learning Approach

Motion Selectivity of the Local Filed Potentials in the Primary Visual Cortex of Rats: A Machine Learning Approach

Using rodents as a model of physiological vision studies requires adequate information about their visual cortex. Although the primary visual cortex of rats has different sub-regions, there are few studies on the different response patterns of these sub-regions. In this study, we recorded the local field potentials (LFPs) from sub-regions of the primary visual cortex (V1) of anesthetized rats. We used random dots patterns as moving stimuli presented in random sequences. Then we used machine learning methods to decode the direction and speed of the stimuli from the recorded signals. Our results revealed that there are different patterns of responses to motion stimuli across sub-regions. Although the decoding results using LFPs were not high, they were enhanced by moving to the lateral sub-regions of the V1. Our results suggested that the location of the recording areas impact reaction time, the pattern of the responses in time- and frequency- domains, and encoding the motion stimuli.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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