Recently I've started reading dynamical systems in neuroscience by izhikevich and was fascinated by the subject. I would like to pursue PhD in computational neuroscience after my masters. Is it worth to choose this area as my Phd topic? or will it lead me to dead end? If possible can some one suggest the leading researchers/labs working in this direction?

  • $\begingroup$ Are you more interested in the neuroscience side of things or in the computation/complex systems side? $\endgroup$
    – qeschaton
    Jul 15, 2018 at 13:40
  • $\begingroup$ Neuroscience side. Particularly dynamical analysis of biological networks and their behaviour. $\endgroup$
    – veerendra
    Jul 15, 2018 at 13:57
  • $\begingroup$ 1) Don't pigeon hole yourself into a particular subfield before you've even made it to grad school. You have no way of assessing what subfield has the most promise at this point, so don't try. Leave yourself open to multiple possibilities of good directions to go. $\endgroup$
    – honi
    Jul 16, 2018 at 2:39
  • $\begingroup$ 2) Dynamical systems and chaos approaches to neuroscience modeling frequently stay too far away from the problems that experimental neuroscience is exploring to be useful for moving forward the broader neuroscience conversation. If you are interested in computational neuroscience, you should always keep in mind that models are unconstrained by reality, and you need to be in extremely close conversation with experimentalists to stay relevant. $\endgroup$
    – honi
    Jul 16, 2018 at 2:41
  • $\begingroup$ 3) The great thing about the dynamical systems approach is that learning differential equations is applicable no matter what type of modeling you do. $\endgroup$
    – honi
    Jul 16, 2018 at 2:41

1 Answer 1


Since this an active and relatively new area of research, nobody can tell you for certain where it will lead.

Whether it will "dead end" is perhaps the wrong way to think about it too. All lines of research have their limits, and when those limits are reached, those researchers who found the limits are often the ones best equipped to move on to the next "thing". If you choose to study it and it turns out to be a less productive area than you had hoped, you will still have learned a large set of useful skills that you can apply to other methods.

As for the usefulness of brain dynamics, the short answer is they have their place and are very promising in certain areas of behavioral science. In particular, coordination and motor control appear to be very well suited to these models. A small but growing body of scientists believe dynamics is the next big thing in cognitive science, though it is still unclear whether this is the case. It sounds like you already have a good primer on the utility of nonlinear dynamics for computational neuroscience with what you're reading. For a more general discussion of their promise and potential for other areas of cognitive science, and a proposal that dynamical systems can essentially replace computational neuroscience, see Chemero (2011). For a concise criticism of the approach see Wagenmakers, van der Mass, and Farrell (2012).

1: Chemero, A. (2011). Radical embodied cognitive science. MIT press.

2: Wagenmakers, E. J., van der Maas, H. L., & Farrell, S. (2012). Abstract Concepts Require Concrete Models: Why Cognitive Scientists Have Not Yet Embraced Nonlinearly Coupled, Dynamical, Self‐Organized Critical, Synergistic, Scale‐Free, Exquisitely Context‐Sensitive, Interaction‐Dominant, Multifractal, Interdependent Brain‐Body‐Niche Systems. Topics in Cognitive Science, 4(1), 87-93.


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