# Measurement effects on critical scaling in neural systems

## TL;DR

This paper shows how different measurement techniques can alter the observed scaling properties in neural systems, affecting interpretations of universal neural behavior.

## Contribution

The study introduces a framework to distinguish scaling effects from neural dynamics versus those introduced by recording methods.

## Key findings

- Scaling properties depend on temporal binning, nonlinearities, and deconvolution.
- Cluster covariance eigenvalues are especially sensitive to measurement effects.
- Scaling exponents change significantly with measurement transformations despite identical neural dynamics.

## Abstract

The recently developed phenomenological renormalization group (pRG) analysis has uncovered scale-free properties in large-scale neural population recordings across recording modalities, including extracellular electrophysiology and calcium imaging. The convergence of these properties across the datasets hints at universal neural behavior. Yet, it is unknown how differences in temporal resolution and measurement details affect pRG scaling. Here, we use a network model known to produce scaling under pRG analysis as a testbed to assess how recording and analysis choices shape inferred scaling exponents. We show that scaling properties depend on the choices of temporal binning, measurement nonlinearities, and deconvolution, and that the quality of scaling for cluster covariance eigenvalues is particularly sensitive to measurement effects. Moreover, all scaling exponents shift substantially with these transformations, even when the underlying neural dynamics are identical. Together, these results show how experimental choices can change pRG scaling and provide a framework for separating scaling driven by neural dynamics from that introduced by the recording method.