Ecker Alexander S, Denfield George H, Bethge Matthias, Tolias Andreas S
Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, 72076 Tübingen, Germany, Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, Bernstein Center for Computational Neuroscience, 72076 Tübingen, Germany, Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany, and
Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030.
J Neurosci. 2016 Feb 3;36(5):1775-89. doi: 10.1523/JNEUROSCI.2044-15.2016.
Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron's gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations, even if unknown to a downstream readout, do not impair the readout accuracy despite inducing limited-range correlations, whereas fluctuations of the attended feature can in principle limit behavioral performance.
Covert attention is one of the most widely studied examples of top-down modulation of neural activity in the visual system. Recent studies argue that attention improves behavioral performance by shaping of the noise distribution to suppress shared variability rather than by increasing response gain. Our work shows, however, that latent, trial-to-trial fluctuations of the focus and strength of attention lead to shared variability that is highly consistent with known experimental observations. Interestingly, fluctuations in the strength of attention do not affect coding performance. As a consequence, the experimentally observed changes in response variability may not be a mechanism of attention, but rather a side effect of attentional allocation strategies in different behavioral contexts.
人们通常认为,注意力通过增加反应增益和抑制神经元群体中的共享变异性来提高行为表现。然而,注意力的焦点和强度在不同的实验试次中可能会有所不同,从而导致实验者未知的反应变异性。在这里,我们通过分析研究注意力状态的波动如何在一个简单的空间和特征注意力模型中影响群体反应的结构。在我们的模型中,注意力仅通过调节每个神经元的增益来作用于神经反应。给定刺激和注意力增益,神经元是条件独立的,并且相关活动仅源于注意力状态的试次间波动,而实验者对此并不知晓。我们发现,这个简单的模型可以很容易地解释注意力下神经反应调制的许多方面,例如反应增益增加、个体和共享变异性降低、与放电率的相关性增加、有限范围的相关性以及差异相关性。因此,我们认为注意力可能主要通过增加单个神经元的反应增益来起作用,而不影响它们的相关结构。实验中观察到的相关性降低可能反而源于在关注刺激时注意力增益变异性的降低。此外,我们表明,注意力增益波动即使下游读出器未知,尽管会诱导有限范围的相关性,但也不会损害读出准确性,而被关注特征的波动原则上可能会限制行为表现。
隐蔽注意力是视觉系统中自上而下调节神经活动的最广泛研究的例子之一。最近的研究认为,注意力通过塑造噪声分布来抑制共享变异性而不是通过增加反应增益来提高行为表现。然而,我们的工作表明,注意力焦点和强度的潜在试次间波动会导致与已知实验观察结果高度一致的共享变异性。有趣的是,注意力强度的波动不会影响编码性能。因此,实验中观察到的反应变异性变化可能不是注意力的一种机制,而是不同行为背景下注意力分配策略的一种副作用。