Emergence of integrated information at macro timescales in real neural recordings

Abstract

How a system generates conscious experience remains an elusive question. One approach towards answering this is to consider the information available in the system from the perspective of the system itself. Integrated information theory (IIT) proposes a measure to capture this, integrated information ({Phi}). While {Phi} can be computed at any spatiotemporal scale, IIT posits that it be applied at the scale at which the measure is maximised. Importantly, {Phi} should emerge to be maximal not at the smallest spatiotemporal scale, but at some macro scale where system elements or timesteps are grouped into larger elements or timesteps. Emergence in this sense has been demonstrated in artificial binary systems, but it remains unclear whether it occurs in real neural recordings which are generally continuous and noisy. Here we first utilise a computational model to confirm that {Phi} becomes maximal at the temporal scales underlying its generative mechanisms. Second, we search for emergence in local field potentials from the fly brain recorded during wakefulness and anaesthesia, finding that normalised {Phi} (wake/anaesthesia), but not raw {Phi} values, peaks at 5 ms. Lastly, we extend our model to investigate why raw {Phi} values themselves did not peak. This work extends the application of {Phi} to simple artificial systems consisting of logic gates towards searching for emergence of a macro spatiotemporal scale in real neural systems.