On the homeostatizing of perturbations
Pupillary dynamics have been established to inform us about a range of behaviours such as mind-wandering (Smallwood et al., 2011, Mittner et al., 2014, 2016) and trust (Kret et al., 2015, Prochazkova et al., 2018). One of the most general hypotheses of the underlying mechanisms is that pupil diameter changes signal fluctuations in arousal by way of fluctuations in norepinephrine signaling from the locus coeruleus (LC-NE; Aston-Jones and Cohen, 2005). In neural network dynamics, the default mode network interacts with LC-NE to form a homeostatizing role for many other intrinsic connectivity networks (Mesulam, 2008, Braga and Leech, 2015, Eldar et al., 2013). Neural annealing through arousal fluctuations by way of LC-NE signaling allows ever more quicker homeostasis in a stable high complexity. Generally the nervous system aims for the homeostatizing of perturbations arriving on its sensory manifold. While the brain is usually hypothesized to reconstruct an internal representation of the three-dimensional (4D when taking into account the representation of more than a single 3D state, i.e. more than one moment, i.e. trajectories in memory) external world by way of sequences of 3D delta matrices, the sensory manifold is more accurately modeled by a 2D torus. Trajectory perturbations on this torus may then be viewed as having four dimensions (two spatial coordinates, inward/outward perturbations, and multiple sequential occurrences). Part of the initial dimensional confusion may arise from this difference between our inference of the external 3D world, and our 2D sensory manifold. As the nervous system is hypothesized generally to homeostatize perturbations, handling a 2D toroidal model allows a more direct and therefore quicker homeostatizing of perturbations than handling a 3D rectilinear model. The nervous system’s measuring of these trajectories of perturbations allows it to anticipate future occurrences of similar trajectories of perturbations, opening the possibility of sending counter-perturbations so as to preserve the non-perturbed state of the toroidal model. That is how the brain homeostatizes perturbations on a toroidal model of the sensory manifold. When a perturbation trajectory is homeostatized only partly by way of the re-occurrence of the perturbation not entirely overlapping with the initially measured perturbation, two things happen: The remaining perturbation is again measured, and further homeostatizing is attempted. Secondly, the perturbation before the initial homeostatizing attempt is reconstructed and is added to memory so as to allow keeping track of commonly occurring perturbation trajectories. Over longer timescales this leads to the homeostatizing of statistical averages of perturbation trajectories rather than single perturbation trajectories. Similarly to the first procedure of homeostatizing a leftover perturbation, residual distributions of perturbation trajectories are iteratively homeostatized. This procedure relates to predictive coding (Huang et al., 2011), bayesian brain (Knill and Pouget, 2014), free energy principle (Friston, 2010) and projective consciousness (Rudrauf et al., 2017). It shows how the brain performs statistical analyses of re-occurring perturbations of the sensory manifold so as to sublimate future occurrences of perturbations into toroidal homeostasis, offering the experience of pure sensory awareness. The accumulation of pure sensory awareness leads to its clear-minded consciousness. This then leads to the observing of the process of homeostatizing of toroidal perturbations, finally allowing its description, its description in turn allowing its own homeostatizing, iteratively into clear sensory awareness.
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