Department of Experimental Medical Science, Neural Basis of Sensorimotor Control, Lund University, Lund, Sweden.
Commun Biol. 2024 Aug 23;7(1):1043. doi: 10.1038/s42003-024-06743-z.
Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population-level signal complexity, we used highly repeatable resolvable visual, tactile, and visuo-tactile inputs and neuronal unit activity recorded at high temporal resolution. We found the state space of the spontaneous activity to be extremely high-dimensional in S1 populations. Their processing of tactile inputs was profoundly modulated by visual inputs and even fine nuances of visual input patterns were separated. Moreover, the dynamic activity states of the S1 neuron population signaled the preceding specific input long after the stimulation had terminated, i.e., resident information that could be a substrate for a working memory. Hence, the recorded high-dimensional representations carried rich multimodal and internal working memory-like signals supporting high complexity in cortical circuitry operation.
复杂性对于自然行为的灵活性和大脑的高效学习非常重要。在这里，我们评估了感觉皮层（S1）中神经元群体的信号复杂性。为了最大限度地提高我们捕获群体水平信号复杂性的机会，我们使用了高度可重复的可分辨视觉、触觉和视触觉输入以及以高时间分辨率记录的神经元单元活动。我们发现 S1 群体的自发活动状态空间具有极高的维度。它们对触觉输入的处理受到视觉输入的强烈调节，甚至视觉输入模式的细微差别也被分离出来。此外，S1 神经元群体的动态活动状态在刺激终止后很长时间内都能指示先前的特定输入，即可以作为工作记忆基础的驻留信息。因此，记录的高维表示形式携带丰富的多模态和内部工作记忆样信号，支持皮质电路操作中的高复杂性。
