Department of Fundamental Neuroscience, Biotech Campus, University of Geneva,1202 Geneva, Switzerland;
Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, 75013 Paris, France.
Proc Natl Acad Sci U S A. 2018 Feb 6;115(6):E1299-E1308. doi: 10.1073/pnas.1714279115. Epub 2018 Jan 23.
Percepts and words can be decoded from distributed neural activity measures. However, the existence of widespread representations might conflict with the more classical notions of hierarchical processing and efficient coding, which are especially relevant in speech processing. Using fMRI and magnetoencephalography during syllable identification, we show that sensory and decisional activity colocalize to a restricted part of the posterior superior temporal gyrus (pSTG). Next, using intracortical recordings, we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine decoding was possible from neuronal activity sampled across different regions of the temporal and frontal lobes, despite weak or absent sensory or decision-related responses. These findings show that speech-sound categorization relies on an efficient readout of focal pSTG neural activity, while more distributed activity patterns, although classifiable by machine learning, instead reflect collateral processes of sensory perception and decision.
知觉和词汇可以从分布式神经活动测量中解码出来。然而，广泛的表示形式的存在可能与更经典的层次处理和有效编码概念相冲突，这些概念在语音处理中尤为重要。我们使用 fMRI 和脑磁图在音节识别期间显示，感觉和决策活动集中在颞上回（pSTG）的一个受限部分。接下来，使用皮层内记录，我们证明该区域的早期和集中的神经活动可以区分正确和错误的决策，并可以通过机器解码来对音节进行分类。至关重要的是，尽管感觉或决策相关反应较弱或不存在，但是从颞叶和额叶的不同区域采样的神经元活动仍可以进行显著的机器解码。这些发现表明，语音分类依赖于对焦点 pSTG 神经活动的有效读出，而更分布式的活动模式虽然可以通过机器学习进行分类，但反映了感觉感知和决策的附带过程。
