Ruddy Kathy L, Cole David M, Simon Colin, Bächinger Marc T
Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Dublin 2, Ireland.
Interdisciplinary Program in Neuroscience,, Utah State University, Logan, Utah, USA.
HRB Open Res. 2020 Jun 4;3:34. doi: 10.12688/hrbopenres.13062.1. eCollection 2020.
The occurrence of neuronal spikes recorded directly from sensory cortex is highly irregular within and between presentations of an invariant stimulus. The traditional solution has been to average responses across many trials. However, with this approach, response variability is downplayed as noise, so it is assumed that statistically controlling it will reveal the brain's true response to a stimulus. A mounting body of evidence suggests that this approach is inadequate. For example, experiments show that response variability itself varies as a function of stimulus dimensions like contrast and state dimensions like attention. In other words, response variability has structure, is therefore potentially informative and should be incorporated into models which try to explain neural encoding. In this article we provide commentary on a recently published study by Coen-Cagli and Solomon that incorporates spike variability in a quantitative model, by explaining it as a function of divisive normalization. We consider the potential role of neural oscillations in this process as a potential bridge between the current microscale findings and response variability at the mesoscale/macroscale level.
直接从感觉皮层记录到的神经元尖峰在不变刺激的多次呈现过程中以及不同呈现之间高度不规则。传统的解决方法是对多次试验的反应进行平均。然而，采用这种方法时，反应变异性被当作噪声而被轻视，因此人们认为通过统计控制它就能揭示大脑对刺激的真实反应。越来越多的证据表明这种方法并不充分。例如，实验表明反应变异性本身会随着诸如对比度等刺激维度以及诸如注意力等状态维度而变化。换句话说，反应变异性具有结构，因此可能具有信息价值，应该纳入试图解释神经编码的模型中。在本文中，我们对科恩 - 卡利和所罗门最近发表的一项研究进行评论，该研究通过将尖峰变异性解释为归一化除法的函数，将其纳入一个定量模型。我们认为神经振荡在这个过程中的潜在作用是当前微观尺度研究结果与中观/宏观尺度水平上的反应变异性之间的潜在桥梁。
