Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA; The Kavli Neural Systems Institute, The Rockefeller University, New York, NY 10065, USA.
Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, NY 10065, USA.
Neuron. 2024 May 15;112(10):1694-1709.e5. doi: 10.1016/j.neuron.2024.02.011. Epub 2024 Mar 6.
The brain's remarkable properties arise from the collective activity of millions of neurons. Widespread application of dimensionality reduction to multi-neuron recordings implies that neural dynamics can be approximated by low-dimensional "latent" signals reflecting neural computations. However, can such low-dimensional representations truly explain the vast range of brain activity, and if not, what is the appropriate resolution and scale of recording to capture them? Imaging neural activity at cellular resolution and near-simultaneously across the mouse cortex, we demonstrate an unbounded scaling of dimensionality with neuron number in populations up to 1 million neurons. Although half of the neural variance is contained within sixteen dimensions correlated with behavior, our discovered scaling of dimensionality corresponds to an ever-increasing number of neuronal ensembles without immediate behavioral or sensory correlates. The activity patterns underlying these higher dimensions are fine grained and cortex wide, highlighting that large-scale, cellular-resolution recording is required to uncover the full substrates of neuronal computations.
大脑的非凡性质源于数百万神经元的集体活动。降维技术在多神经元记录中的广泛应用意味着神经动力学可以通过低维的“潜在”信号来近似，这些信号反映了神经计算。然而，这种低维表示真的可以解释大脑活动的广泛范围吗？如果不能，那么捕获它们的适当分辨率和尺度是什么？通过以细胞分辨率成像神经活动，并在近同时对小鼠皮层进行成像，我们证明了在多达 100 万个神经元的群体中，维度随着神经元数量的增加呈无界扩展。尽管一半的神经方差包含在与行为相关的 16 个维度内，但我们发现的维度扩展对应于越来越多的神经元集合，这些集合没有直接的行为或感觉相关性。这些更高维度的活动模式非常精细，并且覆盖整个皮层，这突出表明需要进行大规模、细胞分辨率的记录，才能揭示神经元计算的全部基质。
