7 ms·

can you say more about the application there? what does the data look like and why is symbolic regression important? asking for a friend ;).

• SubiculumCode 3y ago
  well it was my first thought, and I haven't had my coffee yet. rsfmri data comprise multiple timeseries sampled from voxels throughtout the brain. The dynamics are complex, and there have been attempts to examine it through the lens of attractors etc. I'm not an expert in chaotic analysis, but will say that most of the advances in the field of neuroscience come from innovative analytic methods. Like I said, it was my first thought, but there is a whole subfield examining brain dynamics through these lenses.
  • PaulHoule 3y ago
    That kind of chaos is about simple systems that are unpredictable in the long term. That Lorenz system is just a few equations you could write on a napkin. Whereas your brain has billions and billions of neurons. (You might blame the complexity on having a lot of parts.) That said, another aspect of dissipative chaos is that a highly complex system like turbulence (10^28 or so atoms bouncing off each other) often can be described by an attractor that is rather low dimensional and could be modeled with a few equations. So you might have some neural signal that tools like that could make an interesting model of.
    • sjkoelle 3y ago
      as long as the dynamics are relatively low dimensional, it should be possible to estimate the differential equation, even if it is observed in a high dimensional space