  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.
