Confused about Motivations for Career Goals in Computational Neuroscience

[HizzleT]

Me: double major in applied math. and neuroscience at a big research school.
I want to do research (always have) and I have to decided what Grad. school to go to and what typed of research I want to get into. I think I want to do computational neuroscience -- it's the only way I see (in conjunction with molecular & systems neuroscience) to understand how brains really work.

The problem, however, is that there is a long history of the life sciences fumbling with math. -- largely in an attempt to become "legitimate" in the eyes of the physical sciences. Biological systems are so breathtakingly complicated it is often hard to make accurate mathmatical models of them -- yet we get maligned when we fail to model 200 billion brain cells accurately or put epigenetics into an equation. "Biology is basically arts -- not science" is a phrase that I've often encounter with people in the math department. Anyway, constantly hearing that has an impact. In a way, it kind of stings...

Getting higher grades than the physics majors in my math classes and roommates in engineering only shuts them up. Of course, they still believe what they always have...Some people just love being ignorant and wrong. Yet, I've internalized that ignorance.

In short, my problem is: do I want to do computational neuro., or am I doing trying to appease the physical science people? That is what I am trying to understand...

Has anyone out there experienced similar confusions regarding their motivations for their career goals?

 

[symbolipoint]

Biology is just not very neat. It's very complicated. Some people just hate that kind of complexity and find a great sense of order in the physical sciences (including engineering) and in Mathematics. They make their educational and career choices based on that.

 

[BHL 20]

The problem, however, is that there is a long history of the life sciences fumbling with math. -- largely in an attempt to become "legitimate" in the eyes of the physical sciences. Biological systems are so breathtakingly complicated it is often hard to make accurate mathmatical models of them -- yet we get maligned when we fail to model 200 billion brain cells accurately or put epigenetics into an equation. "Biology is basically arts -- not science" is a phrase that I've often encounter with people in the math department. Anyway, constantly hearing that has an impact. In a way, it kind of stings...

Edit by mentor: insult removed.

Remember, that when people try to put you down for your choice of degree they're just trying to make themselves feel superior to you and reaffirm themselves in their own choice. Obviously, the world would not be what it is if all intelligent people only studied pure mathematics.

There are many applied mathematicians working on things like population dynamics, which obviously hard to model accurately. The key thing in making models for these types of phenomena is to replicate their qualitative aspects, and not to make accurate predictions. For example, the key thing in the Lotka-Volterra model for predator-prey interaction are the cycles of high and low population, where the prey population peaks before the predator population. This is the kind of behaviour we observe in the real world so this can be seen as a good model. This is a simple example, but in more complicated systems formulating a model which captures some of its qualitative aspects can lead to certain insights, similarly to how a physics theory (Maxwell's equations) can describe known phenomena (electromagnetism) while making new predictions (light is an electromagnetic wave).

In my opinion, aspects of biology like evolution and neuron networks are more aesthetically pleasing to model than most physical phenomena. Newtonian mechanics may seem much more rigorous but you really have to look hard to find a mechanical system which displays equally interesting dynamics to say a Hebbian learning model. Not everyone can relate to this though. Some people want hard predictions not just nice dynamics. If you aren't concerned with making hard predictions yourself, I'd say you've nothing to worry about.