Division of Neurobiology, Department Biology II, Ludwig-Maximilians-Universität, Munich, Germany.
PLoS One. 2011;6(9):e24270. doi: 10.1371/journal.pone.0024270. Epub 2011 Sep 13.
The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal.
环境中声源的识别对于许多动物的生存至关重要。然而，这些声音并不是孤立呈现的，因为自然场景由来自多个来源的声音叠加而成。在这种情况下，识别一个声源是一个复杂的计算问题，而大多数动物都能轻易解决。我们提出了一个丘脑中脑皮层回路模型，该模型能够对复杂听觉场景中的听觉目标进行水平不变的识别。该电路可以从可能的元素的大字典中识别出存在的对象，并可靠地对具有多个并发活动源的真实声音信号进行操作。关键模型假设是，一些皮层神经元的活动编码了观察到的信号与内部估计之间的差异。对清醒听觉皮层记录的重新分析显示，神经元的活动模式与这种误差信号相对应。
