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MT 和 MST 中的神经元相关性破坏了随机点运动相反方向的群体解码。

Neuronal Correlations in MT and MST Impair Population Decoding of Opposite Directions of Random Dot Motion.

机构信息

Neuroscience Program, Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, Victoria 3800, Australia.

ARC Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Victoria 3800, Australia.

出版信息

eNeuro. 2018 Jan 9;5(6). doi: 10.1523/ENEURO.0336-18.2018. eCollection 2018 Nov-Dec.

Abstract

The study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.

摘要

对神经元对随机点运动模式的反应的研究为我们深入了解神经元的活动如何与感知相关提供了一些最有价值的见解。在运动方向相反的范式中,通过操纵随机点模式的相干性来降低运动信号的强度,以检验单个神经元的活动如何代表运动方向。为了将该范式扩展到神经元群体,研究人员使用基于来自成对神经元的数据的建模,但几个重要的问题需要进一步研究更大的神经元数据集。我们使用电极阵列在麻醉狨猴的颞中(MT)和内侧上颞(MST)区域记录神经元群体,同时在两个相反的运动方向(左和右)中改变随机点模式的相干性。使用同时记录的神经元的尖峰率,我们使用线性分类器在每个相干性水平上解码运动方向。我们发现相关性的存在对解码性能有不利影响,但与忽略相关性结构的解码器相比,学习相关性结构会产生更好的解码性能。我们还发现,降低运动相干性会增加神经元相关性,但解码器不需要针对每个相干性水平进行优化。最后,我们表明,解码器权重取决于 100%相干性时的左右选择性,而不是首选方向。这些结果对于理解群体神经元编码的信息如何受到尖峰活动中的相关性的影响具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/267e/6327941/030d3564ff56/enu0061828170001.jpg

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