Caveat lector: This blog is where I try out new ideas. I will often be wrong, but that's the point.

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Why computational neuroscience is interesting

Someone on Quora asked me to answer this.

Computational neuroscience is another tool. The brain is a very hard problem, and some scientists try to crack its secrets at the level of synapses and ion channels. Others work on neural firing, or neurotransmitters, or people with brain diseases.

But experimental work only gets us so far. To paraphrase Rutherford, without models, experimental data collection is just stamp collecting. It is often said that neuroscience is "data rich, but theory poor," and that's where the computational neuroscientists come into play.

Yes, "all models are wrong, but some are useful." But without a model to test, what are we doing? Neuroimaging experiments are certainly testing hypotheses, but at the end of the day how useful (or interesting?) is it to say "behavior X preferentially activates brain region A?"

Better would be a model that says that visual cortical neurons learn visual perception through Bayesian probabilistic integration of prior information. This provides a mathematical model and a basis for sensory learning that can then be directly tested experimentally (allowing researchers to hone in on a more "correct" model) that then carries its own predictions about how the brain works.

It's not a "different" way of doing research, it's a powerful complement to experimental work.


"Anti-items" in CVs

Recently I answered the below over on Quora in response to the question, "What is the most important or impressive anti-item on your resume?"

It apparently struck a chord, as I think (?) this is my most well-received response, which is gallows-humor amusing to me because it took me about 90 seconds to write compared to some of my much longer, more thoroughly researched and referenced neuroscientific answers.

But such is the nature of the things. People like stories.

Here's my answer in full. Given that I'm sure this blog has a more academically-oriented readership than the startup-heavy Quora, I'd be curious to hear what people think.


Technically on my CV I state that I'm a two-time Time Person of the Year winner, having split the prize twice: once in 2006 and again in 2011.

More seriously, as an academic scientist our currency are grants and peer-reviewed publications. When I began my PhD I remember looking at the CVs of post-doctoral researchers and faculty whom I admired and seeing page after page after page of amazing awards and scientific achievements. At the time I was struggling to write even a single scientific manuscript and could not understand how anyone could have dozens, or even hundreds.

And then I started getting successful and it occurred to me that I was becoming that thing I could not comprehend. Upon reflection, I realized that hidden behind the plethora of successful outcomes on those CVs I admired were an even greater number of failures.

Therefore I have an entire section of my CV titled "Rejections and Failures" which notes every award for which I've applied or been nominated and lost, every grant or fellowship I did not receive, and noted how many times each paper I published was initially rejected by journal editors.

Hell, my favorite paper and the one that most likely got me my job interview (brainSCANr) was rejected from 13 journals before being published.

Like entrepreneurship, failures in science are learning experiences. You cannot push the boundaries of what is known without some resistance. You can choose to either crumble in face of that, or you can double down and come out the better for those failures.

Unlike entrepreneurship, however, scientists rarely discuss their failures because, it seems, those are viewed as indicators of a lack of excellence. Which is nonsense madness so I'm doing what I can to counter that. From what I've heard from PhD students, this small injection of honesty into the process is very much appreciated.