Laboratory of Neural Information, Graduate School of Brain Science, Doshisha University, Kyotanabe-Shi, Kyoto, 610-0394, Japan.
Research Fellow of the Japan Society for the Promotion of Science (JSPS), Chiyoda-Ku, Tokyo, 102-0083, Japan.
Commun Biol. 2020 Jul 30;3(1):406. doi: 10.1038/s42003-020-01129-3.
Cortical neurons show distinct firing patterns across multiple task epochs characterized by different computations. Recent studies suggest that such distinct patterns underlie dynamic population code achieving computational flexibility, whereas neurons in some cortical areas often show coherent firing patterns across epochs. To understand how coherent single-neuron code contributes to dynamic population code, we analyzed neural responses in the rat perirhinal cortex (PRC) during cue and reward epochs of a two-alternative forced-choice task. We found that the PRC neurons often encoded the opposite choice directions between those epochs. By using principal component analysis as a population-level analysis, we identified neural subspaces associated with each epoch, which reflected coordination across the neurons. The cue and reward epochs shared neural dimensions where the choice directions were consistently discriminated. Interestingly, those dimensions were supported by dynamically changing contributions of the individual neurons. These results demonstrated heterogeneity of coherent single-neuron representations in their contributions to population code.
皮质神经元在多个具有不同计算特征的任务时段表现出不同的放电模式。最近的研究表明,这种不同的模式是动态群体编码实现计算灵活性的基础,而在一些皮质区域的神经元在多个时段通常表现出连贯的放电模式。为了了解连贯的单细胞编码如何有助于动态群体编码,我们在大鼠双侧杏仁旁皮质(PRC)中分析了在双选择强制选择任务的线索和奖励时段的神经反应。我们发现,PRC 神经元在这些时段之间经常编码相反的选择方向。通过使用主成分分析作为群体水平的分析,我们确定了与每个时段相关的神经子空间,这反映了神经元之间的协调。线索和奖励时段共享神经维度,在这些维度中,选择方向被一致地区分。有趣的是,这些维度是由个体神经元的动态变化贡献支持的。这些结果表明,在对群体编码的贡献方面,连贯的单细胞表现出了异质性。