Anyway, here are the paragraphs:
Within the field of theoretical computational neuroscience, there are two general forms in which the problem of cognitive function is mathematically cast: as an adaptive control system and as a dynamical system on the edge of chaos. As with many competing fields of academic thought, disdain from adherents of one mode is often expressed for the ideas of those in the other camp. Fundamentally, the two interpretations are quite similar, as an adaptive controller functions on a dynamical system. However, proponents of the view that the brain functions as a system on the verge of chaos argue that the well-behaved systems generally analysed within the context of control theory fail to take into account the entire activity of the brain and therefore fall short of the goal of generating an accurate physiological model for cognitive function. These proponents also point to the efficacy of mathematical techniques from chaotic and dynamical system analysis to interpretations of electroencephalogram (EEG) readings, which serves as support for the near-chaotic dynamical system interpretation of the brain.I have removed the references, but if anyone is interested in what I am basing the discussion on, let me know and I will send you the appropriate articles.
I would argue, however, that while an adaptive control experiment such as the one being implemented here seeks to isolate and investigate a specific cognitive task irrespective of the rest of the neuronal activity (or, in the case of the simulated robots used in this study, assuming no other neuronal activity), such a blinkered approach is not necessarily done out of ignorance of the larger issues of overall cognitive interconnectivity. Rather, I posit that the near-chaotic nature of the global brain behaviour arises out of the necessity of having many simultaneous well-behaved and sometimes contradictory control loops operating as one. The phase transitions apparent in EEG readings could arise from the necessity of transitioning from one set of precedent control loops to another, and a full understanding of the underlying control loops themselves can thus still further our overall understanding of cognitive function. While admittedly ad hoc, I hope this reasoning may serve to at least somewhat mollify those detractors who would dismiss adaptive control as a convenient tool of engineering misapplied to neuroscience. Continued exploration of adaptive control and implicit supervision can therefore have benefits for the field of theoretical computational neuroscience in addition to direct practical benefits in robotics.