Visual attention mitigates information loss in small- and large-scale neural codes.
Visual attention mitigates information loss in small- and large-scale neural codes.
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DOI：
10.1016/j.tics.2015.02.005
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发表时间：
2015-04
影响因子：
19.9
通讯作者：
Serences JT
中科院分区：
文献类型：
--
作者：
Sprague TC;Saproo S;Serences JT
视觉系统将复杂的输入转换为强大而简约的神经代码，有效地指导行为。由于神经通信是随机的，编码的视觉信息的量必然随着每个突触而减少。这种约束要求以保护有关刺激的信息不被降级的方式处理感觉信号。这种选择性处理-或选择性注意-是通过几种机制实现的，包括神经增益和调谐特性的变化。然而，孤立地检查这些影响中的每一个模糊了它们对大规模群体代码的刺激特征表示的保真度的联合影响。相反，大规模的活动模式可以用来重建相关和不相关刺激的表征，提供关于神经元水平调制如何共同影响刺激编码的整体理解。
The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding.