Sampling effects and measurement overlap can bias the inference of neuronal avalanches

TL;DR

Sampling techniques significantly influence the observed properties of neuronal activity, with coarse sampling potentially producing misleading power-law signatures, while spike recordings provide more accurate insights into underlying neural dynamics.

Contribution

This study systematically compares sampling methods in neural recordings, revealing how measurement overlap and sampling scale bias the inference of criticality in neuronal avalanches.

Findings

Coarse sampling can produce false power-law signatures in non-critical systems.

Spike recordings are less biased and better reflect true neural dynamics.

Overlap in coarse sampling increases correlations artificially.

Abstract

To date, it is still impossible to sample the entire mammalian brain with single-neuron precision. This forces one to either use spikes (focusing on few neurons) or to use coarse-sampled activity (averaging over many neurons, e.g. LFP). Naturally, the sampling technique impacts inference about collective properties. Here, we emulate both sampling techniques on a simple spiking model to quantify how they alter observed correlations and signatures of criticality. We describe a general effect: when the inter-electrode distance is small, electrodes sample overlapping regions in space, which increases the correlation between the signals. For coarse-sampled activity, this can produce power-law distributions even for non-critical systems. In contrast, spike recordings do not suffer this particular bias and underlying dynamics can be identified. This may resolve why coarse measures and spikes have produced contradicting results in the past.