Department of Psychology, Stanford University, Stanford, CA 94305.
Department of Psychology, Michigan State University, East Lansing, MI 48824.
eNeuro. 2019 Mar 26;6(2). doi: 10.1523/ENEURO.0363-18.2019. eCollection 2019 Mar-Apr.
Probing how large populations of neurons represent stimuli is key to understanding sensory representations as many stimulus characteristics can only be discerned from population activity and not from individual single-units. Recently, inverted encoding models have been used to produce channel response functions from large spatial-scale measurements of human brain activity that are reminiscent of single-unit tuning functions and have been proposed to assay "population-level stimulus representations" (Sprague et al., 2018a). However, these channel response functions do not assay population tuning. We show by derivation that the channel response function is only determined up to an invertible linear transform. Thus, these channel response functions are arbitrary, one of an infinite family and therefore not a unique description of population representation. Indeed, simulations demonstrate that bimodal, even random, channel basis functions can account perfectly well for population responses without any underlying neural response units that are so tuned. However, the approach can be salvaged by extending it to reconstruct the stimulus, not the assumed model. We show that when this is done, even using bimodal and random channel basis functions, a unimodal function peaking at the appropriate value of the stimulus is recovered which can be interpreted as a measure of population selectivity. More precisely, the recovered function signifies how likely any value of the stimulus is, given the observed population response. Whether an analysis is recovering the hypothetical responses of an arbitrary model rather than assessing the selectivity of population representations is not an issue unique to the inverted encoding model and human neuroscience, but a general problem that must be confronted as more complex analyses intervene between measurement of population activity and presentation of data.
探究大量神经元如何表示刺激是理解感觉表示的关键，因为许多刺激特征只能从群体活动中辨别出来，而不能从单个单元中辨别出来。最近，反编码模型被用于从人类大脑活动的大空间尺度测量中产生通道响应函数，这些函数类似于单单元调谐函数，并被提议用于检测“群体水平刺激表示”（Sprague 等人，2018a）。然而，这些通道响应函数并不能检测群体调谐。我们通过推导表明，通道响应函数仅由可逆线性变换确定。因此，这些通道响应函数是任意的，是无限个家族中的一个，因此不是群体表示的唯一描述。事实上，模拟表明，双峰甚至随机的通道基函数可以很好地解释群体反应，而无需任何如此调谐的潜在神经反应单元。然而，通过将其扩展到重构刺激而不是假设模型，可以挽救该方法。我们表明，当这样做时，即使使用双峰和随机通道基函数，也可以恢复在适当的刺激值处峰值的单峰函数，该函数可以被解释为群体选择性的度量。更准确地说，恢复的函数表示了在观察到的群体反应下，刺激的任何值的可能性。分析是在恢复任意模型的假设响应，还是在评估群体表示的选择性，这不仅是反编码模型和人类神经科学特有的问题，而且是一个普遍的问题，随着更复杂的分析在群体活动的测量和数据的呈现之间进行，必须面对这个问题。
