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

What is an Experiment?

What is an experiment?

Experimenting with color (source)

People use the term 'experiment' to meant a lot of things. One may say "she experimented with drugs" or "she performs experimental music". Someone might 'experiment with ones hair' or 'perform a thought experiment'.

All of these uses of the word experiment have distinct connotations, but most of them essentially mean 'to try something and see what happens'. In the examples above, most of the phrases also imply that the experiment is something new. If she experiments with her hair, she's probably trying some new style and seeing if she likes it. If she performs experimental music, she's probably not following the conventional rules for the music she is playing.

Are these kinds of 'experiments' different from the experiments that scientists do? Well yes and no.  The basic definition of an experiment as 'trying something [often new] and seeing what happens' is pretty much what scientists do. So what's different? Why isn't someones 'hair experiment' publishable in a scientific journal?

Mythbusters would have you believe that the only difference between science and screwing around is writing it down:


And that is sort of true.

But what really makes a scientific experiment scientific is controls. In our hair example, you can experiment with your hair by dying it black, and seeing if you like it. But that's not the scientific experiment. To be scientific you would have to decide how to measure how much you like your new hair color. You could do this by filling out a survey each day asking you how many times you thought you were pretty or rating your confidence on a scale of 1-10. You could fill this survey out for a week and then dye your hair and fill the survey out for another week. You could then compare the scores and decide if the new black hair had a 'significant' impact on your self-image. 

Lets say it does impact your self image and you report higher self-confidence that second week. But what if you feel different just because you have a new hair color, not because you have black hair?

Well, you would want to do a control experiment, which controls for the newness of the hair color. You could control for novelty by dying your hair yet a different color, and taking the survey for another week. Or you could take the survey two months after you dyed your hair black to see if you still report higher confidence or if your confidence has dropped back down to normal.


This is not a perfect experiment by any means, it's not even a clever or well-designed one, but it is somewhat scientific. And illustrates what I think is the most important difference between experimenting as in trying something new, and experimenting as in trying to find something out:

The control group


In addition, here is a great example of how important the control group is in science. (See the epilogue)

© TheCellularScale

Homeostatic platsicity in a thorny situation

Synapses, the connections between neurons can strengthen and weaken depending on the specific activity at that synapse. This is called synaptic plasticity, and we've talked about it a lot on this blog (here, here, here and here).

the strengthening and weakening of synaptic connections corresponds to the spine growing or shrinking (Matsuzaki 2007)

However, there is another kind of plasticity that can occur at synapses. This is called homeostatic plasticity. And instead of the synapse strengthening or weakening depending on the specific activity at that synapse, the synapses strengthen and weaken in homeostatic plasticity depending on the activity of the whole cell.

To drastically simplify, each cell 'wants' to fire about a certain amount, if it suddenly starts to fire a lot less, it will take steps to strengthen its connections or make itself more 'excitable' so it can get back to its preferred amount of firing. Similarly if the cell starts to fire a lot more than normal, it will take steps to make itself less excitable and to weaken its connections until it reaches the right amount of firing. 

Thorny Excrescences from Lee et al., (2013)

A recent paper from the Pak lab explains how in some specific neurons in the hippocampus (CA3 pyramidal cells), the activity of the whole cell is strongly controlled by a some very peculiar synapses. These synapses are close to the cell body, and are on these HUGE weirdly shaped spines (see above) called "Thorny Excrescences". For comparison 'normal' spines look more like this:

Spines from Lee et al. (2013)

The Thorny Excrescences (TEs) are massive spines that contain many separate synapses on them, but connect to the dendrite through 1 neck. 'Normal' spines, on the other hand, usually have 1 synapse at the spine head, and connect to the dendrite through 1 neck.

The size of the TEs, and their proximity to the soma makes them an extremely powerful way to control the signals that the soma receives. Lee et al (2013) shows that when you drastically reduce activity by blocking action potentials (using TTX), you get massive growth of these TEs, but the normal spines further away from the soma stay the same.

They test 3 things to determine whether the TEs have undergone homeostatic plasticity. They look at the morphology (they are bigger), the activity (the electrical signals from them are bigger) and the molecular signatures (the molecules indicative of new synapses are more plentiful). The paper is a really nice complete story showing that these TEs have a lot of control over the general activity of the cell.

It also solves an important problem with homeostatic plasticity. That is, how can the general activity of the cell be modulated without the specific differences between synapses being erased, and consequently the memories or pieces of information they encode? If homeostatic plasticity occurs at spines dedicated to it, then the other spines can still encode specific signals while the activity of the cell as a whole changes.

© TheCellularScale



Lee KJ, Queenan BN, Rozeboom AM, Bellmore R, Lim ST, Vicini S, & Pak DT (2013). Mossy fiber-CA3 synapses mediate homeostatic plasticity in mature hippocampal neurons. Neuron, 77 (1), 99-114 PMID: 23312519