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

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.Author summary: The criticality hypothesis associates functional benefits with neuronal systems that operate in a dynamic state at a critical point. A common way to probe the dynamic state of a neuronal systems is measuring characteristics of so-called avalanches—distinct cascades of neuronal activity that are separated in time. For example, the probability distribution of the avalanche size will resemble a power-law if a neuronal system is critical. Thus, power-law distributions have become a common indicator for critical dynamics.