Bouchard Kristofer E, Brainard Michael S
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720;
Department of Physiology, University of California, San Francisco, CA 94158; Center for Integrative Neuroscience, University of California, San Francisco, CA 94158; Howard Hughes Medical Institute, University of California, San Francisco, CA 94158.
Proc Natl Acad Sci U S A. 2016 Aug 23;113(34):9641-6. doi: 10.1073/pnas.1606725113. Epub 2016 Aug 9.
Predicting future events is a critical computation for both perception and behavior. Despite the essential nature of this computation, there are few studies demonstrating neural activity that predicts specific events in learned, probabilistic sequences. Here, we test the hypotheses that the dynamics of internally generated neural activity are predictive of future events and are structured by the learned temporal-sequential statistics of those events. We recorded neural activity in Bengalese finch sensory-motor area HVC in response to playback of sequences from individuals' songs, and examined the neural activity that continued after stimulus offset. We found that the strength of response to a syllable in the sequence depended on the delay at which that syllable was played, with a maximal response when the delay matched the intersyllable gap normally present for that specific syllable during song production. Furthermore, poststimulus neural activity induced by sequence playback resembled the neural response to the next syllable in the sequence when that syllable was predictable, but not when the next syllable was uncertain. Our results demonstrate that the dynamics of internally generated HVC neural activity are predictive of the learned temporal-sequential structure of produced song and that the strength of this prediction is modulated by uncertainty.
预测未来事件对于感知和行为而言都是一项关键的计算。尽管这一计算至关重要,但鲜有研究表明神经活动能够预测学习到的概率序列中的特定事件。在此,我们检验以下假设:内部产生的神经活动动态能够预测未来事件,并且由这些事件的学习到的时间序列统计结构所构建。我们记录了孟加拉雀感觉运动区域HVC对个体歌曲序列回放的神经活动,并检查了刺激结束后持续的神经活动。我们发现,对序列中一个音节的反应强度取决于该音节播放的延迟,当延迟与歌曲生成过程中该特定音节通常出现的音节间间隔相匹配时,反应最大。此外,当序列回放诱发的刺激后神经活动在该音节可预测时类似于对序列中下一个音节的神经反应,但当下一个音节不确定时则不然。我们的结果表明,内部产生的HVC神经活动动态能够预测所产生歌曲的学习到的时间序列结构,并且这种预测的强度受不确定性调节。