Compression supports low-dimensional representations of behavior across neural circuits

TL;DR

This paper develops a network theory of neural compression, predicting how different brain regions balance information reduction and capacity, supported by empirical data from youth and neural network models.

Contribution

It introduces a novel model linking activity flow to compression and demonstrates its predictive power across biological and artificial neural systems.

Findings

Dimensionality of activity varies across cortical regions.

Model predicts neural and artificial network capacity with significant accuracy.

Compression emerges from activity flow in distributed neural circuits.

Abstract

Dimensionality reduction, a form of compression, can simplify representations of information to increase efficiency and reveal general patterns. Yet, this simplification also forfeits information, thereby reducing representational capacity. Hence, the brain may benefit from generating both compressed and uncompressed activity, and may do so in a heterogeneous manner across diverse neural circuits that represent low-level (sensory) or high-level (cognitive) stimuli. However, precisely how compression and representational capacity differ across the cortex remains unknown. Here we predict different levels of compression across regional circuits by using random walks on networks to model activity flow and to formulate rate-distortion functions, which are the basis of lossy compression. Using a large sample of youth ($n=1,040$), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks. Our network theory of compression predicts the dimensionality of activity ($t=12.13, p<0.001$) and the representational capacity of biological ($r=0.53, p=0.016$) and artificial ($r=0.61, p<0.001$) networks. The model suggests how a basic form of compression is an emergent property of activity flow between distributed circuits that communicate with the rest of the network.