Friday, August 23, 2013

Computational Psychiatry:Review of Field

In this recent review, Montague et al. cogently reviewed this emerging field. They lucidly described the current state and the future directions for the field. The article is publicly accessible in PMC. I am posting the link below.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556822/pdf/nihms345286.pdf 

Monday, April 13, 2009

Human Time Perception

In a recently published article in Current Opinion in Neurobiology, author David Eagleman stated that time perception is surprisingly prone to measurable distortions and illusions. He further explained how can action and effect be reversed?
To read full text of this very interesting write-up, please click on the link below.

http://neuro.bcm.edu/eagleman/papers/EaglemanCurrOpinionNeuro_TimeIllusions_2008.pdf

Saturday, February 2, 2008

Serotonin, Inhibition, and Negative Mood

In a recent study published in PLoS Computational Biology authors explored the relationship between serotonin, inhibition and negative mood. They also tried to explain contradiction
between the fact that inhibition of serotonin reuptake is the first-line treatment of depression, although serotonin itself is most strongly linked with aversive rather than appetitive outcomes and predictions through mathemetical modelling.
Full article can be found at:
http://compbiol.plosjournals.org/archive/1553-7358/4/2/pdf/10.1371_journal.pcbi.0040004-S.pdf

Tuesday, December 4, 2007

Beyond Gene

In a recent article published in Plos One journal, authors suggested that age old concept "gene" has outlived its utility.
Abstract of the paper stated that paper is a response to the increasing difficulty biologists find in agreeing upon a definition of the gene, and indeed, the increasing disarray in which that concept finds itself. After briefly reviewing these problems, we propose an alternative to both the concept and the word gene—an alternative that, like the gene, is intended to capture the essence of inheritance, but which is both richer and more expressive. It is also clearer in its separation of what the organism statically is (what it tangibly inherits) and what it dynamically does (its functionality and behavior). Our proposal of a genetic functor, or genitor, is a sweeping extension of the classical genotype/phenotype paradigm, yet it appears to be faithful to the findings of contemporary biology, encompassing many of the recently emerging—and surprisingly complex—links between structure and functionality.

Full article can be found at:
http://www.plosone. org/article/ info%3Adoi% 2F10.1371% 2Fjournal. pone.0001231

Sunday, October 28, 2007

“Nine Simple Rules for Doing Your Best Research”

In a recent article published in “PLoS Computatioanl Biology” (Vol. 3, issue October 10, 2007) by Erren et al., they proposed ten rules for the aspiring researchers. In this article I have summarized their views.


“Nine Simple Rules for Doing Your Best Research”

Rule 1: Drop Modesty

To quote Hamming: ‘‘Say to yourself: ‘Yes, I would like to do first-class work.’ Our society frowns on people who set out to do really good work. But you should say to yourself: ‘Yes, I would like

to do something significant.’’

Rule 2: Prepare Your Mind

Many think that great science is the result of good luck, but luck is nothing but the marriage of opportunity and preparation. Hamming cites Pasteur’s adage that ‘‘luck favors the prepared mind.’’

Rule 3: Brains Are Not Enough, You Also Need Courage

Great scientists have more than just brainpower. To again cite Hamming: ‘‘once you get your courage up and believe that you can do important things, then you can. If you think you can’t, almost surely you are not going to. Great scientists will go forward under incredible circumstances; they think and continue to think.’’

Rule 4: Make the Best of Your Working Conditions

To paraphrase Hamming, what most people think is the best working conditions clearly are not, because people are often most productive when working conditions are bad. One of the better times of the Cambridge Physical Laboratories was when they worked practically in shacks—they did some of the best physics ever. By turning the problem around a bit, great scientists often transform an apparent defect into an asset. ‘‘It is a poor workman who blames his tools—the good man gets on with the job, given what he’s got, and gets the best answer he can.’’

Rule 5: Work Hard and Effectively

Most great scientists have tremendous drive, and most of us would be surprised how much we would know if we worked as hard as some great scientists did for many years. As Hamming says: ‘‘Knowledge and productivity are like compound interest. Given two people with exactly the same ability, the one person who manages day in and day out to get in one more hour of thinking will be tremendously more productive over a lifetime.’’ But, Hamming notes, hard work alone is not enough—it must be applied sensibly.

Rule 6: Believe and Doubt Your Hypothesis at the Same Time

Great scientists tolerate ambiguity. They believe the theory enough to go ahead; they doubt it enough to notice the errors and faults so they can step forward and create the new replacement theory. As Hamming says: ‘‘When you find apparent flaws, you’ve got to be sensitive and keep track of those things, and keep an eye out for how they can be explained or how the theory can be changed to fit them. Those are often the great scientific contributions.’’

Rule 7: Work on the Important Problems in Your Field

It is surprising but true that the average scientist spends almost all his time working on problems that he believes not to be important and not to be likely to lead to important results. By contrast, those seeking to do great work must ask: ‘‘what are the important problems of my field? What important problems am I working on?’’ Hamming again: ‘‘it’s that simple. If you want to do great work, you clearly must work on important problems. I finally adopted what I called ‘Great Thoughts Time.’ When I went to lunch Friday noon, I would only discuss great thoughts after that. By great thoughts I mean ones like: ‘What will be the impact of computers on science and how can I change it?’’’

Rule 8: Be Committed to Your Problem

Scientists who are not fully committed to their problem seldom produce first-class work. To a large extent, creativity comes out of the subconscious. If you are deeply immersed in and committed to a topic, day after day, your subconscious has nothing to do but work on your problem. Hamming says it best: ‘‘So the way to manage yourself is that when you have a real important problem you don’t let anything else get the center of

your attention—you keep your thoughts on the problem. Keep your subconscious starved so it has to work on your problem, so you can sleep peacefully and get the answer in the morning, free.’’

Rule 9: Leave Your Door Open

Keeping the door to your office closed makes you more productive in the short term. But ten years later, somehow you may not quite know what problems are worth working on, and all the hard work you do will be ‘‘sort of tangential’’ in importance. He (or she) who leaves the door open gets all kinds of interruptions, but he (or she) also occasionally gets clues as to what the world is and what might be important. Again, Hamming deserves to be quoted verbatim: ‘‘There is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing—not much, but enough that they miss fame.’’