Instituto de Fisiología Celular, Departamento de Neurociencia Cognitiva, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
El Colegio Nacional, Mexico City 06020, Mexico.
Proc Natl Acad Sci U S A. 2024 Jul 16;121(29):e2316765121. doi: 10.1073/pnas.2316765121. Epub 2024 Jul 11.
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.
大脑如何在不产生任何干扰的情况下同时处理互补信息的信号,如原始感觉信号及其转换后的对应信号?当代研究强调了大脑善于利用去相关响应来减少这种干扰的能力。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的概念。然而,原始感觉信号在何处以及如何转化为更抽象的表示仍然不清楚。我们在经过训练的猴子中使用了一个时间模式辨别任务,揭示了第二体感皮层(S2)能够将忠实和转换的神经反应有效地分离到正交子空间中。重要的是,在这个任务的一个不费力的版本中,S2 群体对转换信号的编码,但不是对忠实信号的编码消失了,这表明信号转换及其从下游区域的解码仅在需要时才活跃。一个机制计算模型指出,增益调制可能是观察到的上下文相关计算的一种生物学机制。此外,支撑正交群体表示的个体神经活动表现出连续的反应,没有明确的聚类。这些发现表明,大脑在利用连续的异构神经反应的同时,以依赖于上下文的方式将群体信号分裂到正交子空间中,以提高鲁棒性、性能和提高编码效率。