It takes a semester just to graduate

When my advisor told me that it takes a semester just to graduate, she seriously wasn't kidding. 

Here's the math:

To graduate this December, I had to have everything signed and turned in on December 6th. Doesn't sound so bad, right?

But that basically means I have to defend in November.

And my college/department has a mandatory pre-defense which must be a MONTH before the real defense.

/*Note on the pre-defense: I am not sure how many universities require a pre-defense. It has some pros and cons.
Pros: you have everything in a state of readiness a month before you really need to, and if anything is glaringly horrible and you might not graduate because of it, you find that out before your defense and likely before you tell everyone you are going to graduate. The pre-defense is private, so your presentation is critiqued and likely better for the public real defense, which is to everyone's benefit.  
Cons: you have to have everything ready a month earlier that you really need to. Your committee might use it as an excuse to tell you to do extra things because you have a month. Your committee has to sit through basically the same talk twice, and it is sort of a waste of their time.
end of note*/

Thus the pre-defense must occur in October. 

And your committee needs to read the dissertation before you pre-defend it, so you really need to give it to them 2 weeks before the defense.

This means that to graduate in December, you basically need your dissertation in a state of readability and relative completeness by the end of September!  And since the semester starts at the beginning of September, you have essentially 4 weeks of the fall semester to work on your dissertation.

And the same goes for a spring graduation.
If you want to graduate in May, you defend in April, pre-defend in March, and have have everthing turned in by the end of February.

Plan accordingly.

© DrCellularScale


 

Official SfN Neurobloggers 2013

Due to starting a major simulation this summer, I am not going to the annual society for neuroscience meeting in San Diego this year. And therefore I won't be neuroblogging it like I did last year.

I look forward to reading the posts and tweets from the official neurobloggers this year.

Here they are:

From Brains to Beyonce by @Spork15

House of Mind by  @houseofmind

Neuron Physics by @Eric_Melonakos

NeuroscienceDC by @NeuroscienceDC

Neurolore by @TheMrsZam

NeuroCultureBlog by @LaSaks87

Churchland lab by @anne_churchland

Dormivigilia by @beastlyvaulter

Neurorexia by @ShellyFan

On Psychology and Neuroscience by @astroglia

Más Ciencia por México by @mrenteria_

Imagining Science by @DrImmySmith

Corona Radiata by  @JohnKubie


Follow the #SfN13 hashtag on twitter to find all the unofficial coverage of the conference.


© TheCellularScale

Use Imposter Syndrome to become an excellent grad student

Let's talk about Aristotle for a minute.

School of Athens Aristotle is the one in blue.

Many people mis-attribute this quote to him:

"We are what we repeatedly do. Excellence therefore is not an act, but a habit." -Will Durant

But really this quote is from someone summarizing Aristotle. It's a great summary and it seems to say what Aristotle means, just more concisely.

Aristotle does say:

"For these reasons the virtues are not capacities either; for we are neither called good nor called bad, nor are we praised or blamed, insofar as we are simply capable of feelings. Further, while we have capacities by nature, we do not become good or bad by nature." Nicomachean Ethics Book II 5.5

Ok, so what does this have to do with grad school?

Well lots of people are starting grad school right now with lots of potential. Tons of potential probably, it's what got them into grad school in the first place.

But here's the thing, your potential doesn't mean anything unless you live up to it (or at least come close). Basically Aristotle says that your feelings and intentions and capabilities do not make you excellent, your actions do.

The real lesson here is that you ARE what you DO. if you want to be a good person think 'what would a good person do in this situation?' and then do that thing. Simple really. So in grad school this translates to:

Make Imposter Syndrome work in your favor.

Imposter Syndrome is when someone thinks 'I'm not good enough to be where I am, and I'm just minutes away from the moment my colleagues find out' and it is apparently a plague of many grad students and there are plenty of blog posts around on how to combat it.

But guess what? Playing dress up can make you smarter. People wearing a white coat called a lab coat did better on focus tasks that people wearing the same white coat called an painter's coat (Adam and Galinsky 2012). These are the same people who did the perspective taking experiments showing that when you pretend to be something you become more like it. (See item number 4 on this post.)

Pretending to be what you want to be is actually a completely valid and useful way to become what you want to be. This doesn't mean go into class and pretend you are the professor (that's not a good idea). It means go into class and pretend you are the BEST student in that class. 

So go put on those 'smart person clothes' and make believe that you are the best student that school has ever seen. If you run into a dilemma think to yourself 'what would an excellent grad student do in this situation?' or better yet think 'what would an excellent scientist do in this situation?' and then do that thing.

© TheCellularScale


Adam and Galinsky (2012). Enclothed Cognition Journal of experimental social psychology DOI: 10.1016/j.jesp.2012.02.008

Philosophy of Computational Neuroscience

Just like experimental neuroscience, computational neuroscience can be done well or poorly.

computational models look beautiful (source)

This post was motivated by Janet Stemwedel's recent post in Adventures in Ethics and Science about the philosophy of computational neuroscience. There seem to be three views of the use of computational models in biology and neuroscience:

1. All models are bullshit.
2. Models rely on MATH, so of course they are right.
3. Some models are good and some are bad.

Obviously the first two are extremes and usually posited by people who don't know anything about computational neuroscience, and I am clearly advocating the third view. The only problem is that it is hard to tell if a model is good or bad unless you know a lot about it.

So here are some general principles that can help you divide the good and the bad in computational neuroscience.

1. The authors use the correct level of detail.

devil's in the details (source)

If you are trying to test how brain regions interact with each other, you don't need to model every single cell in each region, but you need to have enough detail to differentiate the brain regions from one another. Similarly, if you are trying to test how molecules diffuse within a dendrite, you don't need to model a whole cell, but you need to have enough detail to differentiate one molecule type from another. If you are trying to test how a cell processes information, you need to have a cell, as you may have learned in how to build a neuron.  Basically a model can be bad simply because it is applied to the wrong question.

2. The authors tune and validate their model using separate data.

When you are making a model you tune it to fit data. For example, in a computational model of a neuron you want to make sure your particular composition of channels produces the right spiking pattern. However, you also want to validate it against data. So how is tuning different from validating? Tuning is when you change the parameters of the model to make it match data. Validating is when you check the tuned model to see if it matches data. Good practice in computational neuroscience is to tune your model to one set of data, but to validate it against a different set of data.
For example, if a cell does X and Y, you can tune your model to effect X, but then check to see that the parameters that make it do X also make it do Y. Sometimes this is not possible. Maybe there is not enough experimental data out there. But if it is not possible, you should at least test the robustness of your model (see point 3).

3. The authors test the robustness of their model.

A robust computational model can be delicious (source)

One problem with computational models is that the specific set of parameters you've found by tuning the model might not be the 'right ones.' In fact they probably aren't the right ones. There are many different sets of parameters that can make a neuron spike slowly, for example.  And the chance that you hit on exactly the correct combination of things is very low. But that doesn't mean the model is not useful. You can still use the model to test effects that are not strongly altered by small changes in these parameters. So you need to test whether the specific effect you are testing is robust to parameter variation. If you are testing effect Q, you can increase the sodium channels by 10%, or the network size by 20% and see if you still get effect Q. In other words is 'effect Q' robust to changes in sodium channels or network size? If it is, then great! Your effect is not some weird fluke due to the exact combination of parameters that you have used.

These are the main things I try to pay attention to, but I am sure there are other important things to keep in mind when making models and reading about them. What are your thoughts?

© TheCellularScale

Treatise on the Diseases of Females: Pregnancy in the 1800s

While looking through some seriously old books, I came across a medical treatise from 1853. Now this would be fascinating on its own, but even better, it's a treatise specifically about the "diseases of females" written by William P. Dewees, M.D.

William Dewees (from Wikipedia)

Having recently been pregnant, I was particularly interested in the 1800s recommendations for pregnancy.

Dewees starts out his chapter on pregnancy by explaining why it is important to scientifically determine whether a woman is pregnant or not. The reasons are essentially as follows:

1. So if the woman needs to be treated for some other disease, she doesn't get prescribed something that would hurt her or the baby if pregnant.
2. Because if she is under trial or awaiting execution, pregnancy might forestall it.
3. If the predicted date of birth might influence the 'character or property' of someone else.

So yes, clearly it is important to know if a woman is pregnant.

So how do you tell in the 1800s when no pee-sticks with plus signs were available? Not surprisingly, the first way is 'she doesn't have her period.' However there is clearly some debate in the field at this time.

Other things can 'suppress the menses' and sometimes a woman can bleed while pregnant.

Dewees spends excessive words and semi-colons defending his position on the subject:

"In declaring that women may menstruate after impregnation, I have no favourite hypothesis to support; nor am I influenced by any affectation or vanity to differ from others; neither do I believe I am more than ordinarily prone to be captivated or misled by the marvellous; for I soberly and honestly believe what I say, and pledge myself for the fidelity of the relation of the cases I adduce in support of my position." *

So you need some other signs of pregnancy other than just not menstruating. Next up: Nausea and Vomiting. Though "far from certain" as a sign of pregnancy, in conjunction with other signs, it is 'added proof'

Another sign is the enlargement of the sebaceous glands (which are on the areolae around the nipple), and the formation of milk. But milk coming in is also not certain:

"I once new a considerable quantity of milk form in the breasts of a lady, who though she had been married a number of years had never been pregnant; but who at this time had been two years separated from her husband. She mentioned the fact of her having milk to a female friend, who from an impression that it augured pregnancy, told it to another friend, as a great secret; and thus, after having enlisted fifteen or twenty to help them keep the secret, it got to the ears of the lady's brother. Her surprise was only equaled by his rage; and, in a paroxysm, he accused his sister, in the most violent and indelicate terms, of incontinency, and menaced her with the most direful vengeance." *

It turns out the lady was not pregnant, but was sick with 'phthisis pulmonalis.'

So finally the surest signs of pregnancy are the enlargement of the uterus and abdomen, and feeling the baby move "quickening".

(also mentioned are the 'pouting of the navel' and the 'spitting of frothy saliva')

*All quotes from Treatise on the Diseases of Females by William P. Dewees

© TheCellularScale

For more on historical pregnancy medicine, see some great posts from Tea in a Teacup. 

Male DNA in the Female Brain

When you are pregnant, people like to tell you all sorts of things about yourself.

probably the most complimentary thing I have been compared to.

"you are going to have a boy/girl"
"you are carrying high/low"
"you look like an olive on a toothpick/beached whale"
"you probably have some of your husband's DNA/baby's cells in your brain now."

huh?

That last one requires a little more explanation. How could new external foreign cells get into my brain? First of all there is the blood-brain barrier which prevents your own blood cells from getting mixed in with your neurons, and second of all there is the placental barrier that prevents your blood from mixing with the baby's blood.

Neither of these barriers are perfect. Certain drugs and chemicals can cross the blood-brain barrier, and drugs and chemicals that a pregnant woman ingests can cross the placental barrier to get to the baby. But are these barriers so leaky that whole cells can get through?

Apparently they are. Dawe et al., 2007 explains possible ways that this can happen.

The placenta, up close. (Dawe et al,. 2007 Figure 1)

The placenta develops with the fetus, and so it is a hotbed of new growing cells early in pregnancy. It is made up of a combination of cells that contain the mother's DNA and cells that contain the new baby's DNA. However it is not clear exactly how baby cells get transferred to the mom. In the author's words:

"The mechanism by which cells are exchanged across the placental barrier is unclear. Possible explanations include deportation of trophoblasts, microtraumatic rupture of the placental blood channels or that specific cell types are capable of adhesion to the trophoblasts of the walls of the fetal blood channels and migration through the placental barrier created by the trophoblasts." (Dawe et al., 2007)

It is also not clear how these baby cells, once in the mother, could cross the blood-brain barrier. In fact, it is not perfectly clear (as of this 2007 paper) that these cells do get into the mother's brain in humans, though studies have shown fetal DNA-containing cells in the brains of mice.

So in conclusion, if you have ever been pregnant, you probably still have some of that baby's DNA (and consequently some of the baby's father's DNA) in your body. If you were pregnant with a boy, then you probably have Y chromosomes in some of your cells! It even seems that mothers can transfer cells from previous babies into future babies. This means that if you have an older brother or sister, you might have some of their DNA in your body as well.


The next question is: Do these foreign DNA cells have a meaningful impact on your body?

© TheCellularScale



Dawe GS, Tan XW, & Xiao ZC (2007). Cell migration from baby to mother. Cell adhesion & migration, 1 (1), 19-27 PMID: 19262088

The Ultimate Simulation

You may have noticed that things have slowed down here at The Cellular Scale.

The reason is that I have been really busy making the ultimate simulation of a human brain. I've worked on this day and night for the past 8 months. It is exhausting work and is starting to take all my energy now that it is nearly complete.

Pretty soon I will have to push this simulation out my uterus, and then all my effort will be spent on simulation support and maintenance. I may write some posts, but they won't be appearing regularly for a while.

© TheCellularScale