Distinguishing states of conscious arousal using statistical complexity
We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data in fruit flies. We connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anaesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anaesthesia. The observed difference complexity between the two conscious arousal states increases as higher orders of temporal correlations are taken into account. In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain, thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner.