Sick Papes

sick papes would like to officially call out those nobel prize winners who only grow their hair out after they got their prize.  i’m embarrassed i even have to write this, you poser shits.  as if you weren’t already getting enough attention.  if we see you in the street we’re apt to de-tail you through the confiscation of your “life is good” scrunchy.  get a grip, last warning.

Nithianantharajah et al. 2012. Synaptic scaffold evolution generated components of vertebrate cognitive complexity. Nat Neuro

 Ryan et al. 2012. Evolution of GluN2A/B cytoplasmic domains diversified vertebrate synaptic plasticity and behavior. Nat Neuro

 Start private browsing.  Under my favorite categories of experiments there on the right you’ll find “gene swapping.” Click on that.  O sweet gene swapping.  I’m back.  It’s like momma earth just spat out DNA here just so experimentalists could do gene swap experiments and fucking rub their hands together and snort and drink coffee and wait for the results.  I should start explaining gene swap experiments in this sentence but I just need to say one more time: in the world of dazzling complexity that is the cell or (eek) tissue or even (eek eek) the whole enchilada, swapping genetic elements offers a straightforward molecular razor for whatever.  Currently we do it one element at a time, but in the future, who knows how many combinations we can apply and track before our brains explode. 

Okay a gene swap experiment is pretty much exactly like it sounds.  Change a single gene in some subtle or not so subtle way to something else.  Make it non-functional say, or maybe just get a mutation that turns the protein in the human form or resistant to a flavor of post-translational modifications or swap subsets of gene-parts to figure out what part of the protein does what.  And it’s slick as shit.   Cause that baby’s siblings don’t have the change and you just compare your normal dude to the mutant you’re studying. Get at that infinitely complex cool and mysterious result stemming from something very discreet you did on the atomic/nanometer scale.  Not bad human experimentalism not bad!

I’m blowing chunks on a couple of back to back Nature Neuroscience boon-diggler gene swap experiments right now: Nithianantharajah et al. and Ryan et al. (2012).  I honestly follow this shit dropping from the Grant lab in the UK but I don’t understand it.  I mean I understand it.  I get their experiments for sure and that they’re trying to use a comparative approach across species to examine the evolution of the synapse and, well, cognition.  But as pretty much the only guys in this business, it’s hard to predict what they’re going to pop out next. 

Nithianantharajah et al (just fyi, it takes 10 ocean mana to tap this character into play but it’s worth it cause he’s got 12 hit points) assay the cognitive capabilities of mice that lack one of four Dlg genes.  The Dlg family constitute major structural components of that sweet little signaling organelle that makes up the receiving half of the excitatory synapse. If the post-synaptic density is a lobster trap, then the Dlgs are the different gauges of chicken wire.  There are four of these boogers because of ancient genome duplications in the vertebrate lineage.  So to understand how each Dlg contributes to cognition is to understand how the duplication of genes allow each dupli-can’t to involve into a dupli-can!:  a twisted sister of it’s own specialized function.  One cool thing about this pape is the authors assay the cognitive prowess of each Dlg mutant mouse by forcing them to play an iPad.  Like our society, but literally thirsty instead of spiritually thirsty. Each individual Dlg knock out showed different cognitive deficiencies suggesting a lack of functional redundancy in each of the 4 genes.  Interestingly, Dlg3 knock out mice showed increased performance in tasks requiring cognitive flexibility and attention, meaning they might have a shot at beating my Mom at bejeweled.  The take home load is that in these knock-out swap experiments, the authors demonstrate that ancient genome duplications allowed for the elaboration of the cognition of mice.  That’s a pretty big rip on theory bong.  Thanks straightforward knock out experiments and tablet computing!

Ryan et al. swap out the intracellular tails of another set of duplicated synaptic genes.  This time the targets are the two main subunits of the NMDA receptors.  NMDA receptors are ion channels that serve as coincidence detectors of neuronal activity and flux calcium, in what is equivalent of a particular synapse sending a text message about it’s state (“party’s on/party sux”) to it’s nearest neighbors and in some cases the friggin nucleus.  The tails of the two subunits are a particularly informed switch since these parts of the proteins are the most divergent and function to bind different swaths of intra-cellular molecules.  So it would seem each tail recruits a different signaling network to respond to calcium.  So how do we test how this tail divergence influences cognition? GENE SWAP and iPADS baby!

So keep in mind: swapping out the tail of sub-unit A onto B means that both proteins have A tails and no B tail exists.  So get double-duty of one tail and a complete lack of duty of the other.  So whatever phenotypes emerge from these swappings could be due to a lack of B or over-binding from A (or synergies in between).  

These dudes conveniently grouped the behaviors that were insensitive to the swap, only sensitive to unidirectional swaps, or sensitive to both swaps.  Only impulsivity related behaviors required having both tails.   Perception, anxiety, coordination and general activity levels required having one tail or the other.  Learning in general remained intact when tails were swapped.  Using the divergent tails of the NMDA receptor tails as a proxy, the authors suggest that more sophisticated regulation of motivational and emotional behaviors was selected for during the early evolution of vertebrates; learning is based on function that is redundant across tails and thus an older phenomenon. 

Wow!  Duh!  And that’s how it goes in the field of synapse evolution

Waters, J., Holbrook, C., Fewell, J., & Harrison, J. (2010). Allometric Scaling of Metabolism, Growth, and Activity in Whole Colonies of the Seed‐Harvester Ant Pogonomyrmex californicus The American Naturalist, 176 (4), 501-510 DOI: 10.1086/656266/>

We all know the feeling: You’re lying naked in a sun-soaked field after taking a fistful of mushrooms and watching waves of energy explode through your friends’ braincases. And no matter how long you watch the trees breathe, just can’t shake the question: “Where does my body end and the world begin?” Turns out this cosmic question has a hallowed tradition, and just about no one knows how to draw boundaries around a body. 

The little guys that fucks with our best minds most royally on this distinguished issue are the social Hymenoptera (ants, bees, and wasps). Dudes have been flubberbusting long and hard about whether we should think about the bees in a hive (or people in a city, or dicks in a game of dick jenga) as a wonderful communion of separate beings or as all just the dangly bits of one MegaMan. As the disturbing old saying goes, there’s many ways to skin a cat, but what perverted shitbag wants to to skin a cat a bunch of different ways? So the world was on the verge of turning its back forever on this age old question and exploding in a supernova of its own ignorance.

That is until some brave souls (Dr. James Waters and colleagues) figured out the illest of ways to blow the lid off a part of this problem. But let me slow my roll a bit and fill in the rubbly background that makes it crystalline just way this pape is so sick:

For just about forever, we’ve known one thing about bodies for sure: how fast they use up energy (their metabolic rate) has a crazy strong relationship with how big they are. Specifically, bigger things use less energy per unit body mass than small things. So basically, one giant 100 kg rat should be using up energy much slower than 100 puny 1 kg rats, even though the grand total of rat meat is the same in both cases.

So, what these dudes did was investigate this same problem in ants, the superest of superorganisms. In an ant colony, you should be able to predict how much energy the whole colony is using based on their average body mass (e.g. you should be able to just sum up the metabolic rate of a bunch of small ants). But when they put whole colonies of these little guys in a fancy box that measures how fast they’re using up their cosmic energies, turns out they’re doing exactly not that. Specifically, their metabolic rate is what you’d predict for a single organism that had the collective mass of all the ants. And metabolic rate changes with colony size the same way it does for bigger bodies. So, in summary, ants (a) are fucking crazy, and (b) on both the mystical and physical planes appear to be working just like a single, physically integrated body does. Why? Lord knows. But this paper is opening up ways to answer that question and new ways to think about the most basic aspects of how organisms are put together. Sick.

Oh, and ants do this too.

Eliasmith, C., Stewart, T., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., & Rasmussen, D. (2012). A Large-Scale Model of the Functioning Brain Science, 338 (6111), 1202-1205 DOI: 10.1126/science.1225266

The H. P. Lovecraft novella At the Mountains of Madness is a story about scientific hubris, and the insignificance of human achievement when confronted with the vastness that is the cosmos. The central characters are scientists searching the Antarctic for novel geological or biological forms. They uncover a world of strange, ancient beings, seemingly preserved in the ice. Their discovery has profound implications for the nature of biological evolution, and perhaps the history — and future — of earth itself. The primary entity they find is a god-like tentacled, winged, gilled monstrosity with strange geometrical features. We are given this account:

“It had digestion and circulation, and eliminated waste matter through the reddish tubes of its starfish-shaped base … The nervous system was so complex and highly developed as to leave [the scientist] aghast. Though excessively primitive and archaic in some responses, the thing had a set of ganglial centers and connectives arguing the very extremes of specialized development. Its five-lobed brain was surprisingly advance and there were signs of a sensory equipment, served in part through the wiry cilia of the head, involving facts alien to any other terrestrial organism … It was partly vegetable, but had three-fourths of the essentials of animal structure.”

I had a similar reaction when reading about S.P.A.U.N., the monstrous creation described by Eliasmith et al. in their pape: “A large-scale model of the functioning brain”. The title alone suggests a new and scary precipice of human achievement. And a most sick pape. 

S.P.A.U.N. is half way between a robot and a computer program. It has a small camera attached to its head, and a robotic appendage extending from an implied torso that is capable of drawing all manner of digits between 0 and 9. Its brain has multiple sub-systems that independently control the encoding of visual input, reward, working memory, decoding, and motor output. The thrust of Eliasmith et al.’s pape is not that any one component of S.P.A.U.N. is new, but that it can perform not just one task but a variety of tasks, all of which humans can perform, and that its modular architecture resembles and realistically models at least some aspects of the human brain. 

How close does S.P.A.U.N. come to resembling the brain? Each of its systems are meant to correspond to cortical and sub-cortical brain areas or functions, though much of the correspondence seems superficial. For example, S.P.A.U.N. has a system for handling “Visual Input”. It’s implied that it corresponds to areas V1, V2, V4, and IT of the primate visual pathway. But S.P.A.U.N. cannot mimic known processing in all those areas because we still don’t know what they do! The visual system of S.P.A.U.N. also reveals that its individual neurons are not as realistic as you might think. The authors stress that it uses biologically-realistic neurons with neurotransmitter dynamics, but most of the visual system instead uses simple linear combinations and thresholding. It’s hard to evaluate these shortcuts because the system is so complex.

So S.P.A.U.N. can do some cool tricks and kind of maybe looks like a brain. Can it fight a bear in one-on-one combat? Definitely not. S.P.A.U.N.‘s proponents admit that it is not the most impressive robot. When I googled “what is the sweetest robot?” I found this strawberry-picking robot. It uses 3D image processing to evaluate ripeness, and can delicately pluck luscious plump strawberries from their stems. That seems more impressive than S.P.A.U.N.

Becoming the best robot is not S.P.A.U.N.’s goal. What is the goal? Building AI systems that perform complex, physical, interactive tasks with flexible, modular systems resembling known biology, that could in principle help us understand how brains work. A longstanding, and recently popularized but criticized, tradition in AI is to build systems that have vast representational power, memory, and fancy statistical learning algorithms, but generally produce simple outputs. For example, a computer vision system that encodes a complex image but only needs to decide which of 10 different kinds of objects it is looking at. But most animals evolved to do more than spit out a 10-bit vector. They engage in real-time with a dynamically changing world. S.P.A.U.N.’s complex motor output and working memory is a step forward, but its emphasis on symbolic reasoning — which number comes next in a sequence? — is excessively abstract. If such symbolic reasoning evolved as an abstraction of more directly-physically-realized functions — when can I jump to make sure I catch the next mouse? — why not first try to build robots that can accomplish those tasks? I recommend the exciting research program of the “embodied robotocist” Rodney Brooks, especially his efforts in the 1990s to build robots with distributed control systems for path finding and navigation. He also appears, looking entirely crazed, in this movie, which features robots, lion taming, tree grooming, and naked mole rats. We are truly standing beneath the Mountains of Madness.