神经元
缩放比例
神经科学
降维
维数之咒
计算
感觉系统
皮质(解剖学)
神经网络
计算机科学
生物
模式识别(心理学)
生物系统
数学
人工智能
算法
几何学
作者
Jason Manley,Sihao Lu,Kevin M. Barber,Jeff Demas,Hyewon Kim,David G. Meyer,Francisca Martínez Traub,Alipasha Vaziri
出处
期刊:Neuron
[Cell Press]
日期:2024-03-06
卷期号:112 (10): 1694-1709.e5
被引量:16
标识
DOI:10.1016/j.neuron.2024.02.011
摘要
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.
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