Abstract
We apply techniques from the field of computational mechanics to evaluate the statistical complexity of neural recording data from fruit flies. First, we connect statistical complexity to the flies' level of conscious arousal, which is manipulated by general anesthesia (isoflurane). We show that the complexity of even single channel time series data decreases under anesthesia. The observed difference in complexity between the two states of conscious arousal increases as higher orders of temporal correlations are taken into account. We then go on to show that, in addition to reducing complexity, anesthesia also modulates the informational structure between the forward- and reverse-time neural signals. Specifically, using three distinct notions of temporal asymmetry we show that anesthesia reduces temporal asymmetry on information-theoretic and information-geometric grounds. In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, thus heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia also modulates the temporal asymmetry of neural signals. Together, our results demonstrate that anesthetized brains become both less structured and more reversible.
Highlights
Complex phenomena are everywhere in the physical world
In contrast to prior work, our results show that: (1) Complexity differences can emerge at very short timescales and across broad regions of the fly brain, heralding the macroscopic state of anesthesia in a previously unforeseen manner, and (2) that general anesthesia modulates the temporal asymmetry of neural signals
We find that the asymmetry in information structure between forward and reverse-time neural signals is reduced under anesthesia
Summary
Complex phenomena are everywhere in the physical world. Typically, these emerge from simple interactions among elements in a network, such as atoms making up molecules or organisms in a society. Machines account for multiple temporal correlations contained in the data and can be used to quantify the statistical complexity of a process—the minimal amount of information required to specify its state. As such they have been applied over various fields, ranging from neuroscience [23,24] and psychology [25] to crystallography [26] and ecology [27], to the stock market [28]. Using the nuanced characterisation of temporal information flow offered by the machine framework [33], we analyze the time irreversibility and crypticity of the neural signals to further distinguish the conscious states. IV, we begin with a brief overview of the machine framework we will use for our analysis
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