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Abstract：
How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain\'s adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.
摘要：
大脑如何同时处理带来互补信息的信号，像原始的感官信号和它们转化的对应物，没有任何破坏性干扰？当代研究强调了大脑在使用去相关反应来减少这种干扰方面的熟练程度。神经生理学发现和人工神经网络都支持信号区分和并行处理的正交表示概念。然而，where,以及如何将原始感官信号转化为更抽象的表示形式尚不清楚。在受过训练的猴子中使用时间模式辨别任务，我们发现第二个体感皮层（S2）有效地隔离了忠实的神经反应，并将其转化为正交子空间。重要的是,用于变换信号的S2群体编码，但不是忠实的人，在此任务的非要求版本中消失了，这表明信号转换和来自下游区域的解码仅是按需活动的。机械计算模型指出增益调制是观察到的上下文相关计算的可能的生物学机制。此外，构成正交群体表示基础的个体神经活动表现出连续的反应，没有确定的集群。这些发现表明大脑,在采用一系列异质神经反应的同时，以上下文相关的方式将种群信号拆分为正交子空间，以增强鲁棒性，性能，提高编码效率。
