Neurophotonics Center, Boston University, Boston, Massachusetts, United States.
Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States.
J Neurophysiol. 2023 Sep 1;130(3):775-787. doi: 10.1152/jn.00148.2023. Epub 2023 Aug 30.
Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single-neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex code competing sounds have not been previously investigated. Here, we apply a novel information-theoretic approach to estimate information in populations of neurons in mouse auditory cortex about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the labeled line code outperforming the summed population code and approaching information levels attained in the absence of competing stimuli. Finally, information in the labeled line code increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in humans and animals. Taken together, our results reveal that a compact population of neurons in auditory cortex provides a robust code for competing sounds from different spatial locations. Little is known about how populations of neurons within cortical circuits encode sensory stimuli in the presence of competing stimuli at other spatial locations. Here, we investigate this problem in auditory cortex using a recently proposed information-theoretic approach. We find a small subset of neurons nearly maximizes information about target sounds in the presence of competing maskers, approaching information levels for isolated stimuli, and provides a noise-robust code for sounds in a complex auditory scene.
皮层回路对感觉信息的编码由神经元群体组成，但信息如何通过汇集单个细胞进行聚合仍知之甚少。这种汇集在单个神经元编码受到干扰的嘈杂环境中可能尤为重要。一个例子是鸡尾酒会问题，多个空间位置的声音相互竞争。听觉皮层中神经元群体如何对来自多个空间位置的竞争声音进行编码，以前尚未研究过。在这里，我们应用一种新的信息论方法来估计小鼠听觉皮层中神经元群体对来自多个空间位置的竞争声音的信息，包括总和种群（SP）和标记线（LL）编码。我们发现，一小部分神经元就足以在不同的空间配置下几乎最大化互信息，标记线编码优于总和种群编码，并接近在没有竞争刺激的情况下达到的信息水平。最后，标记线编码中的信息随目标和掩蔽之间的空间分离而增加，与人类和动物的空间掩蔽释放行为结果相吻合。总之，我们的结果表明，听觉皮层中的一小部分神经元为来自不同空间位置的竞争声音提供了稳健的编码。关于在其他空间位置存在竞争刺激的情况下，皮层回路中的神经元群体如何编码感觉刺激，人们知之甚少。在这里，我们使用最近提出的信息论方法在听觉皮层中研究了这个问题。我们发现，一小部分神经元在存在竞争掩蔽的情况下几乎最大化了目标声音的信息量，接近孤立刺激的信息量，并为复杂听觉场景中的声音提供了一种抗噪编码。
