Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada.
Department of Physiology, McGill University, Montreal, Quebec H3G 1Y6, Canada
J Neurosci. 2021 Apr 28;41(17):3822-3841. doi: 10.1523/JNEUROSCI.2232-20.2021. Epub 2021 Mar 9.
Natural stimuli display spatiotemporal characteristics that typically vary over orders of magnitude, and their encoding by sensory neurons remains poorly understood. We investigated population coding of highly heterogeneous natural electrocommunication stimuli in of either sex. Neuronal activities were positively correlated with one another in the absence of stimulation, and correlation magnitude decayed with increasing distance between recording sites. Under stimulation, we found that correlations between trial-averaged neuronal responses (i.e., signal correlations) were positive and higher in magnitude for neurons located close to another, but that correlations between the trial-to-trial variability (i.e., noise correlations) were independent of physical distance. Overall, signal and noise correlations were independent of stimulus waveform as well as of one another. To investigate how neuronal populations encoded natural electrocommunication stimuli, we considered a nonlinear decoder for which the activities were combined. Decoding performance was best for a timescale of 6 ms, indicating that midbrain neurons transmit information via precise spike timing. A simple summation of neuronal activities (equally weighted sum) revealed that noise correlations limited decoding performance by introducing redundancy. Using an evolution algorithm to optimize performance when considering instead unequally weighted sums of neuronal activities revealed much greater performance values, indicating that midbrain neuron populations transmit information that reliably enable discrimination between different stimulus waveforms. Interestingly, we found that different weight combinations gave rise to similar discriminability, suggesting robustness. Our results have important implications for understanding how natural stimuli are integrated by downstream brain areas to give rise to behavioral responses. We show that midbrain electrosensory neurons display correlations between their activities and that these can significantly impact performance of decoders. While noise correlations limited discrimination performance by introducing redundancy, considering unequally weighted sums of neuronal activities gave rise to much improved performance and mitigated the deleterious effects of noise correlations. Further analysis revealed that increased discriminability was achieved by making trial-averaged responses more separable, as well as by reducing trial-to-trial variability by eliminating noise correlations. We further found that multiple combinations of weights could give rise to similar discrimination performances, which suggests that such combinatorial codes could be achieved in the brain. We conclude that the activities of midbrain neuronal populations can be used to reliably discriminate between highly heterogeneous stimulus waveforms.
自然刺激呈现出时空特征，这些特征通常跨越多个数量级变化，而感觉神经元对其的编码仍然知之甚少。我们研究了两性的电感受器中高度异质的自然电通信刺激的群体编码。在没有刺激的情况下，神经元的活动彼此之间呈正相关，而相关程度随记录部位之间距离的增加而衰减。在刺激下，我们发现，试验平均神经元反应（即信号相关）之间的相关性在彼此靠近的神经元中为正且相关性更大，但是，试验到试验变异性（即噪声相关）之间的相关性与物理距离无关。总体而言，信号和噪声相关性与刺激波形以及彼此之间均无关。为了研究神经元群体如何对自然电通信刺激进行编码，我们考虑了用于组合活动的非线性解码器。对于 6ms 的时间尺度，解码性能最佳，这表明中脑神经元通过精确的尖峰定时来传输信息。简单地对神经元活动进行求和（加权和）表明，噪声相关性通过引入冗余来限制解码性能。使用进化算法来优化性能，同时考虑神经元活动的不等加权和，揭示了更高的性能值，这表明中脑神经元群体传输的信息可可靠地区分不同的刺激波形。有趣的是，我们发现不同的权重组合会产生相似的可辨别性，表明了稳健性。我们的结果对于理解下游大脑区域如何整合自然刺激以产生行为反应具有重要意义。我们表明，中脑电感觉神经元之间的活动存在相关性，这些相关性会显著影响解码器的性能。尽管噪声相关性通过引入冗余限制了辨别性能，但考虑神经元活动的不等加权和会产生更高的性能，并减轻噪声相关性的有害影响。进一步的分析表明，通过增加试验平均响应的可分离性以及通过消除噪声相关性来减少试验到试验的变异性，可以实现更高的辨别力。我们进一步发现，多种权重组合可以产生相似的辨别性能，这表明这种组合代码可以在大脑中实现。我们的结论是，中脑神经元群体的活动可用于可靠地区分高度异质的刺激波形。
