Neural Mechanisms and Functional Purpose of Dynamic Dimensionality States in Sensory Cortex

在过去的十年中，大脑活动的测量已经发展到同时记录快速增长的神经元数量。引人注目的是，这些大规模的记录表明，大脑活动通常可以使用比记录的细胞数量少得多的变量来有效地描述，因此将神经活动限制在低维空间中。据观察，这种网络特征的大小是动态变化的，当我们注意力集中、感知新信息或记忆过去的经验时，这种变化会发生变化。尽管观察到这些与任务表现的相关性，但对网络维度如何影响感觉处理的基本理解仍然缺失。此外，还不知道是什么生物网络特性，如网络连接或不同细胞类型的平衡，引起了这种动态现象。我们将通过结合对小鼠大规模钙记录的数据分析和复制这些特征的新人工模型的开发来解决这些问题。总之，这些方法将产生维度如何因果地影响我们对周围世界的感知的可验证的假设，我们将通过在不同维度状态下扰乱小鼠的大脑活动来测试。简而言之，我们将对网络的内部状态（有效维度）如何调节网络的计算能力有一个低层次的理解。这种网络特征对生物和人工系统都是至关重要的，如果没有对其机制的定量理解，我们对如何理解周围环境的知识就会受到严重限制

Over the past decade, measurements of brain activity have advanced to record a rapidly growing number of neurons simultaneously. Strikingly, these large-scale recordings have revealed that brain activity can often be effectively described using far fewer variables than the number of recorded cells, hence confining the neural activity to a low-dimensional space. The magnitude of this network feature has been observed to change dynamically, varying when we pay attention, perceive new information or memorise past experiences. Despite these observed correlations to task performance, a fundamental understanding of how network dimensionality influences sensory processing is missing. Additionally, it is unknown what biological network properties, such as network connectivity or the balance of different cell types, give rise to this dynamic phenomenon. We will address these questions by combining data analysis of large-scale calcium recordings from mice with the development of new artificial models that replicate these characteristics. Together, these approaches will yield testable hypotheses of how dimensionality causally influences the perception of the world around us, which we will put to the test by perturbing brain activity in mice during different dimensionality states. In short, we will develop a low-level understanding of how an internal state of a network, effective dimensionality, can modulate the network's computational capacity. This network trait is fundamental to biological and artificial systems alike, and without a quantitative understanding of its mechanisms, we critically limit our knowledge of how to make sense of our surroundings