The Informatics Axon News - Headlines
G Protein-Coupled Receptors (GPCRs) are my favorite receptors in the whole wide world. Their versatility allows them to modulate a remarkable number of signaling pathways; environmental stimuli such as light, neurotransmission signals like acetylcholine, and hormonal stimuli such as adrenaline, all utilize GPCRs.
GPCRs are localized on plasma membranes and activated by extracellular ligands. However, recent studies have opened the door to the possibility that GPCRs exist and function INSIDE the cell -- HOW THE! -- is exactly what a group from Washington University in St. Louis (a.k.a. Wash-U) has been exploring...
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A subclass of glutamate receptors (mGluR5) have been shown to be located on intracellular and nuclear membranes of striatal neurons and HEK cells. The question arises as to what are the signals that mobilize mGluR5 to the nuclear vs. the plasma membrane. Using chimeras (mGluR5 - and a related plasma membrane receptor - GABABR2), the Wash-U trio of Ismail Sergin, Vikas Kumar and Karen O’Malley determined that a string of amino acids located near the mGluR5 C-terminus play an important role in localizing or retaining this receptor on inner nuclear membranes. They speculate that this region might be involved in a process that actively weaves it through the nuclear surface.
- But wait, there’s more!
How are GPCRs located inside the cell activated? Using sponge constructs that buffer IP3 signaling, they experimentally determined that glutamate most likely is able to traverse cell membranes and activate these intracellular receptors. How bout that!
- That’s not all!
Part of the beauty of OneSci, is that those researchers who are kind enough to provide us with a poster rE-print allow our readers the opportunity to view the detailed experimental methods in vivid color. So, although Jeremy has completely trumped all the other editors covering the 2009 SfN conference, both in quantity and (I’ll let you be the judge of) quality -- I now present to you… THE POSTER FYI: be sure to click on the poster image to enhance
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Flu Season
The common cold can strike at any time. But there are certain periods throughout the year where the prevalence of illness goes way up. These peak times usually come during the cold winter months, which over the years has led to much popular speculation about flu transmission. Probably the most repeated adage is that the cold weather somehow makes us susceptible to catching a cold (I mean, it is called a cold, after all). Braving the weather without the proper clothing??? Boy, you’ll get sick. Going outside with wet hair??? You’re done for. But with just a little knowledge of microbiology and physiology, the logic of these beliefs start to break down. Sure, protecting yourself from the elements is sound general advice, but just how the heck does exposure to the cold stir up those flu virons within?
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Apologies to all the moms out there, but throwing on a jacket before you go out in the cold probably does very, very little to thwart those pesky viruses. So why then is there a flu season, and why does it always seem to coincide with the winter months?
A more reasonable explanation is that during cold weather, people have less of a tendency to be outside, and a greater tendency to cohabitate closed quarters. That means offices, classrooms, buses, etc. will all have their windows and doors closed, which means greater circulation of the air within, which means grater dispersion of that bacterial infection from the gent coughing up a storm a few seats away. So, yes, indirectly, cold weather creates an atmosphere conducive for the spread of illness.
New research suggests there may be a more direct link as well. Recent studies in the Proceedings of the National Academy of Science and PLoS Biology indicate that atmospheres with low humidity enhance survival of airborne influenza virons, which means the virus mr. sneezey has unleashed upon the environment will survive just a bit longer during the low humidity months associated with winter – perhaps just long enough to find a nice new host to embed itself within. Adding more weight to the authors’ assertion, flu epidemics over the last 30 years were shown to correlate with the onset of periods with unusually low humidity.
So, if you really want to avoid getting sick, trade in that jacket and scarf for a good face mask and indoor humidifier. Or, if you prefer not to look like a freaking weirdo (in public, at least) you could just suck it up and endure the inevitable.
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 BDNF - Waaay cooler than you If there were a competition to determine the current biological “wonder molecule,” brain-derived neurotrophic factor, or BDNF, would have to be among the finalists. Found in the central nervous system, BDNF is important for neuronal growth and differentiation, and has been shown to stave off neurodegeneration in animal models of Alzheimers [1]. Additionally, BDNF appears to play a vital role in LTP and memory.
Back in 2007, Bekinschtein et al published an article in Neuron showing that maintenance of a recently acquired associative-learning task required BDNF synthesis 12 hours after learning [2]. That is, BDNF appeared to be necessary for consolidation of the memory, though not for its initial formation. But as the title of this post would indicate, this story can't all be about our hot little BDNF molecule. Dopamine has got to enter the picture somewhere, right?
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Fast forward to the present. Following up on these earlier findings, Rossato and colleagues now report that dopaminergic signaling in CA1 may underlie the delayed upregulation of BDNF necessary for long-term memory (LTM) formation [3]. In this impressive study – wherein everything seemed to fall nicely into place (why can’t it work that way for the rest of us?) – the authors show that administration of a D1 dopamine receptor antagonist faithfully blocked LTM of a fearful association when given 12 hours following learning. Interestingly, this antagonist had no effect on LTM if given immediately or 9 hours after learning, indicating that D1 signaling may underlie the increased expression of BDNF previously reported. In a series of follow-ups, the authors drilled into the D1 signaling pathway to flesh out the processes involved, ultimately supporting the conclusion that dopamine regulates expression of BDNF.
A cool little addition to this study is that, normally, weak training only causes a memory to persist for a couple days (short-term memory), whereas strong training will trigger LTM (>14 days). Both short-term and long-term memory require dopaminergic signaling during initial learning. However, only strong conditioning produces the 12-hour delayed D1/BDNF signaling that leads to prolonged memory. What if weakly trained animals had D1 signaling artificially induced 12 hours later??? The memory is preserved for the long haul, that’s what. Or, to put it another way, Rossato et al were able to take a weak memory and artificially boost its strength (unfortunately for the rats, the memory was a fearful one).
So why dopamine? As you may have read in previous posts on The Axon, dopamine is believed to denote stimuli or events of high motivational value. Thus, it makes sense that stimuli eliciting strong dopamine release would support formation of lasting memories for said stimuli. How early and delayed dopamine signaling are linked remains an open question.
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One of the central predictions of the Hebbian theory of learning is that memories are stored by the same neurons that were engaged during learning. Although Hebb published his famous postulate, “neurons that fire together, wire together” (paraphrasing, of course) over 50 years ago, actual evidence that both learning and memory activate the same population of neurons has largely been absent. However, new research out of the Hausser lab lends experimental support to this long-standing assumption, and also indicates that reactivation of just a fraction of these “memory neurons” is enough to conjure up full blown recall.
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Just a few years ago, Reijmers et al genetically manipulated mice to enable tagging of neurons active at two distinct timepoints: during encoding and during retrieval of a specific memory. Their results showed that a significant number of neurons were active at both time points when compared to chance, suggesting the same neurons that are active during learning are also active during recall. Now, Rizzi et al have taken another leap forward by showing that if neurons engaged during learning are reactivated at a later time point, the memory of the learned content is triggered.
How they accomplished this is actually quite simple, though wonderfully elegant. I briefly discussed channelrhodopsin-2 (ChR2) in an earlier post, which is probably the most popular technique in the burgeoning field of optogenics. Briefly, channelrhodopsin is a light-activated cation channel that causes rapid depolarization of cells whenever light of a particular wavelength is encountered. Linking the channelrhodopsin gene to specific gene promoters enables researchers to express ChR2 in a particular cell population, or, in the case of an immediate-early gene promoter, exclusively in active cells. Hausser and colleagues used the latter approach, linking ChR2 to the c-fos promoter. The next step was to administer a task where the brain structure mediating learning is well known. They accomplished this using contextual fear conditioning, which is hippocampal dependent. So, theoretically, hippocampal neurons active when animals are learning the association between foot shock and environment will express ChR2, and thus can be activated by shining light into the hippocampus at a later point in time. And when light was delivered to this region in behaving animals, they froze, an indicator of fear – presumably stemming from recall of the fearful memory.
What’s also remarkable is that the researchers were able to elicit a fear response by activating a very small (~100) number of neurons, corresponding to just a fraction of the neuronal population activated during learning. Does stimulation of a few neurons enable activation of the complete network of neurons encoding the memory (similar to a pattern completion function)? Does incomplete reactivation cause incomplete recall? Are memories stored in redundant networks, where stimulation of just one of these is enough to activate recall? Or is it simply that within a single network there exist several backups of a memory, expressed as multiple neurons encoding the same information?
Previous work from Han et al has shown that memory for a fearful event remains intact following inactivation of up to 20% of the neurons engaged during learning, suggesting that multiple memory traces exist. However, other interpretations certainly can’t be ruled out just yet.
A few caveats, of course. Anytime you are using immediate-early gene expression as a metric of neuronal activation, you must be careful. Several of these genes exist, and all seem to be expressed under specialized conditions and time points. But, expression of genes like c-fos in the hippocampus does show reliable correlation to things like contextual fear learning. Also, I wonder how long the c-fos–ChR2 gene was “expressible” during the experiment. Obviously, the hippocampus is a pretty active structure, and background activity or exposure to extraneous learning opportunities could increase expression of ChR2 and dilute specific expression due to contextual fear conditioning. Nevertheless, freakin’ cool study.
Usually, I like to include a link to the paper of interest so y’all can read it for yourselves, but in this case, such a paper does not yet exist. The best I can do for you is a copy of the abstract from their poster at this year’s SfN conference in Chicago, which goes a lot like this:
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The mammalian brain is capable of storing information in sparse populations of neurons encompassing several brain areas. Immediate recall of this information is possible upon presentation of a cue or context. Most aspects of this process remain unresolved: are the cells involved in information storage also responsible for its recall? What portion of this distributed circuit needs to be reactivated, in order to achieve successful recall? To answer these questions we selectively expressed a genetically encoded optogenetic probe (Boyden et al., 2005) in neurons engaged during the learning of a specific association. A plasmid encoding channelrhodopsin-2 and EGFP under an immediate early gene promoter (c-fos-ChR2-IRES-EGFP) was electroporated in vivo into granule cells (GCs) of the dorsal dentate gyrus of anaesthetized C57BL/6 mice. Mice were allowed to recover, and then underwent classical delay fear conditioning (consisting of 10-20 pairings of a 5 second auditory tone and a 2 second footshock). An optic fiber was implanted intra-cranially to allow optical stimulation of transfected neurons. Light stimulation (λ = 530 nm; 5 Hz) successfully induced recall of the fear memory, measured as freezing behaviour (n = 27 animals). Post-hoc analysis of the transfected tissue revealed that a remarkably small subpopulation of GCs (<~100 cells) was sufficient to cause this effect. We then tested whether any, comparatively sized, subset of GCs could be equally effective. We transfected neurons with a plasmid encoding ChR2 expression under a general promoter (pCAG-ChR2) to obtain ChR2 expression in a random population of cells. Interestingly, optical stimulation of this population was insufficient to induce memory recall (population data: n=30). Our results therefore suggest that recall of a learned association, sparsely stored in neuronal circuits distributed over several brain areas, can be achieved by the simple reactivation of a very small subset of neurons involved in learning this association. Furthermore, our strategy may also be useful for dissecting the complexities associated with memory storage and recall.
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| —Rizzi et al., SfN 2009
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Relocating the Engram
Over 50 years ago, the scientific community was introduced to the fascinating case of Henry Molaison. Better known to neuroscientists by the initials HM, Molaison lost function of his hippocampus following surgery for intractable seizures, rendering him unable to form any new (conscious) memories. But HM was not completely devoid of all memory. In fact, his memory for childhood events was rather keen, suggesting that this hippocampus structure may be necessary for forming new memories, but may not be where memories are ultimately stored. And thus began a massive effort to understand the role of the hippocampus in memory formation and consolidation. Since then, a vast number of studies have strengthened the hypothesis of a time-limited role for the hippocampus in memory formation. The latest entry to this continuing saga comes from a study published in The Journal of Neuroscience
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The research, authored by Restivo et al, examined the time course of spine formation in the hippocampus and anterior cingulate cortex following contextual fear learning in mice. Based on the widely held hypothesis that declarative memories are “formed” in the hippocampus and slowly transferred to the cortex and other structures (where they are ultimately stored), one might predict that spine density would be increased in the hippocampus soon after learning, and in the cortex only following a long delay (that is, after the memory no longer requires the hippocampus for retrieval). (Confused???). Not one to disappoint, Restivo et al showed that this is indeed the case.
More specifically, they showed increased spine growth in the hippocampus 24 hours after a single session of fear conditioning, whereas spine density in the cortex remained unchanged at this time point. Conversely, 32 days after fear conditioning, spine density was no different from pseudo-conditioned control animals in the hippocampus, but significantly increased in the cortex. When the hippocampus was lesioned immediately after conditioning, both memory and cortical spine growth were impaired. When hippocampal lesions were performed 24 days post conditioning, however, both memory and increased cortical spine density were intact, suggesting the “transfer” of the memory from hippocampus to cortex had been completed. Unfortunately, a cortical-lesion control was not performed.
The study highlights several interesting questions in the field. For example, when does a particular memory become hippocampal-independent, and how exactly does this happen? One explanation offered by the authors is that, “top-down inhibitory control, presumably arising from cortical regions which are actively engaged in remote memory storage and retrieval” might serve to disengage the involvement of the hippocampus. An additional (and admittedly highly speculative) idea is that neuronal turnover in the hippocampus could place a time limit on the consolidation process, i.e. when neurons coding for a specific memory eventually die off or become integrated in a circuit with newborn cells, the consolidation process also ceases.
An additional question one might ask is, “if the hippocampus is so paramount for all forms of conscious memory, and animals are constantly being exposed to new events and presumably forming new memories, why the heck would one see increased spine density in the hippocampus solely after fear conditioning?” This I don’t have a good answer for, chiefly because it’s my question! But it could be that something as traumatic and rapidly acquired as fear conditioning results in enhanced plasticity in the hippocampus. Regardless, the study adds weight to the idea that the hippocampus is fundamental for transferring and consolidating memories to external structures, and even hints at part of the engram for contextual fear conditioning.
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Advance Publication
There is an advertisement on the Science homepage that sometimes catches my eye. The little sliver of an ad features a couple of gummy-looking dudes all running to some elusive goal, with the caption underneath, “It’s not just what you know, but when you know it.”
Hard to argue with that. In science, if you’re second, well you’re just replicating someone else’s work, and good luck getting that published. It doesn’t matter if your path was independent of that other guy, he’ll still get all the credit (just ask Alfred Russel Wallace). And it’s not just in science, but in business, medicine, and even mate selection (sorry gals, taken). So it seems that in this world there’s not just a premium on knowing, but knowing as soon as possible. And apparently this holds for monkeys staring at strange shapes while strapped to a chair.
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In a recent study published in Neuron, Bromberg-Martin and Hikosaka delivered either large or small juice rewards to monkeys while different shapes flashed on a computer screen in front of them. Although the number of small and large rewards was held constant, the monkeys could learn the magnitude of the next reward if they so desired. The monkeys almost always chose to learn the identity of the impending reward ahead of time, despite not being able to alter the outcome in anyway by doing so. Furthermore, monkeys elected to learn this information as early on in the trial as possible. This, by itself, would not be too noteworthy. Advanced knowledge could be used for preparatory actions, or at least to relieve any anxious uncertainty about the trial’s outcome. However, the authors were also recording from midbrain dopamine neurons (a system which you may recall from a previous post here on The Axon).
We have long known that the degree of activation of these midbrain neurons depends on the rewarding value of a stimulus (although other hypotheses that dismiss a dopaminergic role in reward processes do exist). Thus, it is no surprise that the level of activity of these midbrain neurons was correlated with the magnitude of the reward. What is new, however, is that these same neurons also showed increased firing whenever the monkey was given the chance to learn about the level of the upcoming reward. One might liberally interpret this as the acquisition of advanced knowledge being an intrinsically rewarding event (or at least the opportunity to acquire such knowledge). So, it seems that not only do we seek out knowledge in a timely manner to avoid being scooped by our closest competitor, but such information seeking may be inherently wired into our behavior.
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 The Morris Water Maze - Nightmare for rodent and graduate student alike Love it or hate it, the Morris Water Maze is a staple of most memory labs. If you’re a PI, you probably love it. If you’re a graduate student, undergrad, technician, or any other poor soul that actually has to run the damn thing, it’s very likely that you hate it. And for how widespread its use is, it certainly has many, many limitations. Don’t tell me that plunking a rat into a pool of milky water, with an invisible platform as his only means for escape, is a pure measurement of spatial memory. The stress alone is enough to confound your measurement. But as the saying goes, it’s the worst test for spatial memory we have, except for every other test in existence…
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And amidst this backdrop, the creator of the Morris Water Maze, the (in)famous Richard Morris, took the stage for the SfN Presidential Special Lecture last Sunday (Oct 18). Part of me was anticipating some disgruntled grad student start chucking wet rats at the stage, while another was excited to hear the musings from one of the greatest minds in the field. But I began to lose interest in both prospects as he slowly trudged through decades of memory research with his punctilious British accent. I’m glad I held on though, because when past gave way to present, he introduced some very exciting research that challenges what some consider dogma in learning and memory research.
Basically, he set out to refute two long-held beliefs by claiming: 1) under particular scenarios, memories can be formed to weak, sub-threshold input (that is, input unable to form a memory by itself); and 2) cortical learning – which is believed to take place only after many repetitions or long durations of time – can be rapidly acquired if new learning is integrated into a previously held network of cortical memories (i.e., an existing schema).
The first statement I didn’t find that revolutionary, as we’ve known for a long time that super-threshold stimuli paired with sub-threshold stimuli can cause a memory for the weak stimuli to be formed. However, his interpretation was quite different and actually rather novel.
The claim? Memory formation requires both a tag and interacting ligands. The former can be elicited by strong or weak stimulation. In contrast, only strong stimulation can bring about the latter.
The evidence? LTP was blocked (via CaMKII inhibition) in strongly stimulated pathways. However, when a weak stimulus was applied at a later time point in a separate pathway, this weakly stimulated synapse was potentiated.
The explanation? The strong stimulation causes expression of ligands that interact with CaMKII, leading to LTP. Usually, weak stimulation does not lead to ligand creation (or perhaps ligand liberation). But because the ligands were available due to the previous strong stimulation (and perhaps still present because they were inhibited from binding within the “strong pathway”), they bound to tags present in the weak pathway, leading to potentiation of theses synapses. Beautiful.
His second claim employed a clever behavioral paradigm that would take too long to get into here (good news though: it in no way involves water). But the gist is that if animals are able to integrate new information into an existing, well-learned schema, then this new knowledge can be incorporated into the cortex very rapidly. This learning is still hippocampal-dependent, however, as lesioning the hippocampus before or directly after the task abolishes learning. But the role of the hippocampus is remarkably transient (only a day or so, I believe).
After delighting us with these recent insights, he moved toward the future of memory research, and, like all great scientists before him, he has set his sights on locating the legendary engram. And while I’m hopeful that he will bring about great advances in this ongoing search, like all great scientists before him, he will probably fail to find it (as I’m sure I will if I ever take up the learning and memory torch that intrigues me so).
So, there you have it. Despite creating one of the most hated behavioral tests of our time – and being British – his brilliance and keen intellect is undeniable (just kidding, Brits). No real surprise, of course, but it’s exciting when giants of the field still have some tricks up their sleeve.
This will be my last “update” from SfN 2009 (though entries from other members are yet to come...or so I'm told). But I encourage all of you to come back in the future. Our goal here at OneSci is to get a lively, intelligent discussion going about science and research. And not just others’ research, but your research. Signing up only takes a minute, and we’ll never give your info away. Ever. (The profits we’re after with this site aren’t of the monetary kind.) And if you have a poster you presented at this year’s conference, for god’s sake, put that up on the site and give all those who didn’t catch it the first time around a second opportunity. The comments you get just might lead to a pivotal insight! And speaking of comments, you should leave some comments. I don’t post here and in The Axon simply because I enjoy one-sided conversations, but I want to bolster my knowledge with other viewpoints. Your viewpoints! Plus, feedback helps sustain anybody working in isolation, so comment away (you don’t even need to be a member to do so). Alright, pitch over ;)
Write the rest of your news article here, not to be mistaken for the .
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A New Role For Vitamin P
For many years, selective serotonin reuptake inhibitors (SSRIs) such as prozac have been a favorite pharmalogical treatment for depression. Interestingly, it is often weeks before the psychological effects of these drugs kick in, the cause of this lag period being largely unknown. As the drug is known to increase neurogenesis, some believe this upregulation of newborn cells - and the gradual time it takes for this process to occur - underlies the delayed impact of SSRIs.
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In a "recent" study published in Science magazine, Vetencourt et al throw another interpretation into the hat. Their study indicates that under the influence of fluoxetine (aka prozac), the adult rat visual cortex can undergo levels of reorganization typically restricted to developmental periods of life. This enhanced plasticity appears to be mediated by a decrease in inhibitory GABAergic signaling (an increase in which is known to coincide with the end of the critical period during development).
The authors ostensibly focus on how fluoxetine can be used as a treatment for amblyopia, a condition where signaling from one eye is impaired due to input deprivation during development. However, considering many systems-level plastic changes often take weeks to emerge in adult animals, one immediately wonders if reorganization of particular emotional areas of the brain might underlie the mood-enhancing effects of SSRIs (and, if so, why are such effects unipolar in their psychological consequence?). Or perhaps it is an overabundance of inhibitory signaling that SSRIs rectify?
Whatever the case, given what we already know about plasticity and sensory processing, you might consider increasing your vitamin p intake next time you take on a new language (I hear the side effects are pretty tolerable).
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(Purge articles)
On the existence of god
As a neuroscientist, I'm always a bit surprised when a colleague believes in god. I mean, our pursuit as neuroscientists is to uncover the rigid physical laws that govern biological processes and behavior, which doesn't leave much room for things like free will. Of course, the absence of free will doesn't preclude the existence of god, but it certainly challenges the commonly held view of a Judaea-Christian God. And while I don't feel it's my place to dissuade others from their beliefs, I am always receptive to a good debate.
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And in that spirit, kudos to ABC's Nightline for their segment last night, "Does God Have a Future?" In it, they had some heavyweight atheists and spiritual gurus face off on the topic of the existence of god during a public debate held at Cal Tech. The debate got slightly heated at times, as one might expect anytime one's deep-seated, guiding beliefs are challenged. But, it also made for good television.
Replay from the show can be seen here (The entire debate is also available on the nightline website): http://abcnews.go.com/nightline/video/faith-doubt-10186250&tab=9482930§ion=1206872&playlist=10185323
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The following article is featured on the NPR news website, along with a nice little video.
Harald Wolf of the University of Ulm and his assistant Matthias Whittlinger proposed that ants have "pedometer-like" cells in their brains that count the steps they take.
How Do Ants Get Home?
Most ants get around by leaving smell trails on the forest floor that show other ants how to get home or to food. They squeeze the glands that cover their bodies; those glands release a scent, and the scents in combination create trails the other ants can follow.
That works in the forest, but it doesn't work in a desert. Deserts are sandy and when the wind blows, smells scatter.
So how do desert ants find their way home?
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It's already known that ants use celestial clues to establish the general direction home, but how do they know exactly the number of steps to take that will lead them right to the entrance of their nest?
Wolf and Whittlinger trained a bunch of ants to walk across a patch of desert to some food. When the ants began eating, the scientists trapped them and divided them into three groups. They left the first group alone. With the second group, they used superglue to attach pre-cut pig bristles to each of their six legs, essentially putting them on stilts.
The third group had their legs cut off just below the "knees," making each of their six legs shorter.
After the meal and the makeover, the ants were released and all of them headed home to the nest while the scientists watched to see what would happen.
The "Pedometer Effect"
The regular ants walked right to the nest and went inside.
The ants on stilts walked right past the nest, stopped and looked around for their home.
The ants on stumps fell short of the nest, stopped and seemed to be searching for their home.
It turns out that all the ants had walked the same number of steps, but because their gaits had been changed (the stilty ants, like Monty Python creatures, walked with giant steps; the stumpy ants walked in baby steps) they went exactly the distances you'd predict if their brains counted the number of steps out to the food and then reversed direction and counted the same number of steps back. In other words, all the ants counted the same number of steps back!
Does that mean ants have something like pedometers that do something like counting?
Says professor James Gould of Princeton, commenting on the experiment: "These animals are fooled exactly the way you'd expect if they were counting steps."
Gould says it's pretty clear ants don't have maps in their heads and don't recognize markers along the route. This experiment strongly suggests that ants do have internal pedometers that allow them to "count" their way home.
Special thanks to OddTodd, our animator, and to comedian Jessi Klein, who provided ant voices in our video.
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Is Free Will and Illusion?"
A response evoked by (not directed at) this essay published in Nature
Our editor for The Axon, Jeremy Biane had idea for an appropriate definition of free will, that I am going to expand upon. Let's call this theory the “everything being equal - test," which could be both the definition and the experiment. It’s my contention that it would prove humans, and probably most animals, have free will. But I can only imagine this two-option choice test that I have in mind, being anything other than theoretical. It would be one in which the test apparatus could adjust to balance the sum of the antecedent forces influencing the test-subject.
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Lets assume the test is measuring whether an entity can chose to go right or left. Suppose the apparatus is a round platform that slides along a track; a computer is used to control the movement of the platform. Say, a rock is released from 3 feet above the platform, at which point the computer calculates for the necessary adjustments, and a sliding mechanism puts the platform on the direct coordinates crunched by the computer. The rock should land on the sloped platform at end up in equilibrium.
At this point, it is likely that the rock is not going to make any further decision about which way it should go, so it is assumed that a rock does not have the ability to choose left over right, and thus does not have free will. If you could make a similar apparatus to test humans, one that takes into consideration a far greater number of antecedents beyond gravity, normal force, velocity, etc. - such as handedness, emotional state, hunger, tiredness, and every other minuscule physical property – then it might be possible to run an appropriate test. These factors could be entered into the computer controlling the round platform. The human participant jumps onto the platform from above but not before the computer slides the platform into place, at the exact point where all antecedent variables influencing a decision between left and right have been taken into consideration. This would create a sort of ultimate equilibrium between the participant and its two options. It is predicted that, if the human does not have the capacity of free will, it would be incapable of choosing left or right (much like the stone).
You can recognize this dilemma in home computers as a kernel error, followed by your operating system crashing. The human, I foresee, simply making a random choice. It is necessary that the choice be random, otherwise it would be predictable, and our computer program failed to consider every variable influencing the decision.
Things I’m left musing about:
We are affected by events that do not contact us physically. The manner by which a rock will tumble down a slope is determined by its shape; which was formed by forces of direct impact. Humans, however, have a shape that is formed by forces that I do not contend to be “direct impact.” A man gets stung by a bee (direct). A boy witnesses it (light), processes it (electrochemical), writes it down (symbolic), a friend reads it (light)(interpretive), and stores it (electrochemical). The bee sting took energy to directly impact the man, but that energy did not emit light, the light was simply available from other sources. Thus, the impact of the bee sting on the boy, was merely gleaned through symbolic interpretation – something uniquely intrinsic to this sole boy. The energy that went directly into the bee sting would not be useful for predicting future behavioral decisions of the boy (even though the boy is likely to have learned from the event, and to modulate his future behavior based on this information).
I think life, then consciousness, stems from the incalculable efficiency by which conscious organisms are able to transduce and manipulate energy. A complex understanding can be awakened by several photos borne before the human eye.
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One of the most prevalent assumptions we make as neuroscientists is that the brain communicates using a binary code. That is, either a neuron fires and action potential and passes along information (1), or it doesn’t (0). Presumably, information is stored and conveyed by the pattern of neurons that are active at any particular time. For example, let’s say we have 10 neurons. Neurons 2,4,6 and 8 represent an apple, while neurons 3,5,6 and 9 represent a banana. If 2,4,6,and 8 are active at one time point, we identify an apple. If only neurons 2,4 and 8 fire, we would probably still identify an apple due to activation of the entire network via pattern completion. But the point is a neuron has to fire an action potential in order to convey information. Right?
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Most computational models build off this ‘all or none’ assumption, and they seem to do a decent job in representing some phenomena. But why would it have to be this way? What is so special about this transient fluctuation in membrane potential, and what about it allows images and events to enter our awareness? Is it the opening of voltage-gated ion channels? Release of neurotransmitter? Rapid movement of ions? Do action potentials that originate at the hillock have different effects than those initiated in the dendrites?
I cannot wait for the day when we have the technology to begin to address some of these questions. And I think we’re close. With techniques like 2-photon imaging and calcium uncaging, we could theoretically “excite” neuronal boutons such that neurotransmitters are released (via Ca2+ binding) without an action potential ever taking place in the cell. Conversely, we could block neurotransmitter release and electrically stimulate cells. Granted, information is almost certainly held within vast networks of cells, so experiments like these on the single-cell level are futile. But enormous progress is being made in identifying neuronal ensembles active during a particular behavior or memory (for example, see this week's J Neurosci article from Dombeck et al). And if we can identify the neural correlates of a particular memory, we might be able to apply techniques similar to those mentioned above to this population.
But the question still remains: what the hell is so special about action potentials? And can postsynaptic potentials that fail to reach threshold still convey information at the level of awareness?
I also need to mention a wonderful review article by Alcino Silva and colleagues in a recent issue of Science, titled Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. In particular, there was one assertion that thoroughly confused me.
In the article, a model is presented for how two or more memories closely associated in time might be stored, such that retrieval of one memory enhances the probability of retrieving the other (see figure). To quote the article, “two memories acquired within minutes of each other may be stored  Can proximity of strengthened synapses affect co-recall? How does the proximity of synapses (on the same neuron) have anything to do with the likelihood of co-recall??? Based on the binary action potential assumption above, an information signal is based on whether a neuron fires or not. And if a neuron fires, it should have the same effect on all it's afferent connections. Far as I know, activated spines do not send out retrograde signals that potentiate surrounding synapses on an immediate time scale. So, what am I missing???
Seriously, I’m asking you. YOU, person reading this right now. You’ve read this far, so you must be interested. The only thing I can come up with is that events occurring in close temporal proximity lead to increased probability of potentiating synapses on the same neuron. And if there is large overlap of neurons that make up the networks representing events A and B, then the probability of activating network A will increase the probability of activating network B. But in this scenario the proximity of these synapses should not matter, so long as the synapses reside on the same neuron. WTF???
One last question: could the originating location of an EPSP be a source of information?
Comments?
Synaptic tagging?
Without reading the review, could they not be talking about events after the action potential has fired? When the first memory is triggered by activation of a certain set of synapses, molecular events at these synapses can alter them in some way to modify the probability they will fire next time. If the specificity of these molecular events is not perfect, synapses in close proximity to the activated set, may also get tagged or modified, thus changing their firing probability, which in turn will affect the ability to recall the associated memory. All the synapses are still firing all-or-none (don't worry, the universe didn't just turn upside down!) but events after the initial action potential can influence a local region of dendrite, which will include the synapses of the second memory. This paper from the Svoboda lab talks about how molecular tagging events are sometimes not localized to just the activated synapse but can spread a little way along the dendrite. This may not be what you were getting at Jeremy, but it's the only explanation I could come up with! -Clare
Hmmmmm, still don't see it
Bloody hell. Just responded via word press, but lost it due to a dang error page. Let's try again.
Yeah, I see where you're going with this, and I think it's an excellent explanation of why two events in close temporal proximity (A and B) would be stored in neighboring synapses. But I fail to see how this would affect co-recall of these two memories. In my mind, co-recall is the process where retrieval of memory A increases the probability of activation of memory B, and this takes place on the scale of sub-seconds. However, the passive diffusion of agents that Svoboda discusses takes place on order of seconds, yes? Now, I could see how retrieval of memory A could facilitate retrieval of memory B at some point in the future, but co-recall? Maybe I'm misinterpreting their definition of co-recall (or your response)?
But even if the diffusion and influence of these potentiating agents were instantaneous, would that really matter? In this case, activation of memory A might make it easier to independently recall memory B, but it wouldn't activate memory B, and thus no co-recall. I suppose I could find some scenario using reverberating loops or something where this might work, but I don't think that's what the authors had in mind.
Something ain't right. Either I'm totally missing something, my base assumptions of how the brain operates are fundamentally flawed, or the review is mistaken. I suspect it's the first.
Jeremy Biane
Comment on Qualitative Information
Bradley Monakhos 23:16, 9 November 2009 (UTC)
 Fig 1. There are about 100 million photoreceptors in the human retina and only 1 million ganglion cells; on the average, 100 photoreceptors must be connected to 1 ganglion cell. Everything in our environment comes to us through waves of energy. When a wave contacts a surface, two piece of information can be coded temporally - Frequency and Amplitude. Coding the frequency and amplitude is enough to transmit qualitative information about a stimulus. In the visual system for example, qualitative information must somehow be transmitted through modulation of neuron firing patterns because of the quick convergence of stimuli information passed from photoreceptors to ganglion cells (see Fig 1). Although, spatial information is stored retinotopically, qualitative information must some how be conveyed by information stored in the frequency of neuronal firing patterns. This is the same for light, sound, touch, pain, etc.
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Dees Noodle Need Sauce
I know, you need a source right. Here's a little snip from a researcher at the RIKEN Institute. If you don't know what RIKEN is, it's the MIT of Japan. In fact, many of the faculty at MIT and RIKEN have joint appointments at both institutes. Be sure to check out the RIKEN-MIT Center for Neural Circuit Genetics.
Enter Toshihiko Hosoya:
“It has been believed that a single neuron usually transmits a single piece of information. However, doesn’t that seem wasteful?” says Hosoya. “Information will be transmitted more efficiently if multiple pieces of information are carried in one signal. This also forms the fundamental basis of information processing theory.”
Therefore Hosoya decided to investigate extensively what information is transmitted by the retinal neurons. He used the neuron that responds to OFF, which ‘fires’ (generates an electrical signal) when it becomes dark. First, he repeatedly stimulated the retina using an image with changing dark areas, and examined the timing of the firing of the neuron that responds to OFF. It used to be thought that the responses of the retinal neurons involved a wide temporal fluctuation, with the timing of the firing differing between events, even in response to the same stimulation. In recent years, however, it has become evident that firing occurs with high reproducibility in terms of time in response to the same stimulation. Hosoya showed experimentally that the firing events in response to the same stimulation occurred with only a small fluctuation of several thousandths of a second. “This is quite an intriguing feature,” says Hosoya. “Because the retinal neurons fire with high reproducibility in response to the same stimulation, it has become easy to analyze what kind of information is conveyed when the neuron fires.”
 Fig 2. Responses of retinal neurons. Firing (green points) of OFF-responding neurons in the salamander retina on stimulation by the brightness change shown by the gray line. This represents data for a single neuron receiving the same brightness change for 21 iterations. Firings caused by each iteration are shown in a row; cases of three firings are colored. The intervals between the first and second firings and between the second and third firings are found to be highly reproducible between all iterations. The retina neurons that respond to OFF fire more frequently as the image input becomes darker and darker. “It has long been known that the firing frequency changes depending on ‘how dark it has become,’ or on the amplitude of the intensity change. Extensive examination shows, however, that both the firing frequency and the time interval are quite accurate (Fig. 3). This seems to carry some information.”
Hosoya theorizes as follows. “The neurons may transmit information on ‘how it has become dark’ as well as on ‘how dark it has become’.” It has also been demonstrated that accurate firing patterns are transmitted from the retina to the brain through the optic nerve. Upsetting conventional common sense, it may be shown that a single neuron transmits multiple pieces of information by using an accurate firing pattern.
Commnet on 'Comment on Qualitative Information'
Ok, good foundational argument for your rate-encoding system. The conclusion of Hosoya seem quite intuitive to me, so I don't know what they're talking about when they say 'upsetting conventional common sense.' (Maybe I possess uncommon common sense?) It's easy to see how onset of firing encodes when a stimulus is present, while firing rate encodes amplitude. But I find it harder to relate this to internal circuits that code for something like a memory. No, wait, maybe I can.
Ok, in 'my' system where each neuron in the circuit encodes a particular aspect of the memory, firing rate could simply encode amplitude of that aspect. For instance, with our black neuron (see discussion below), rate of firing would encode the amplitude of black of the object. This scenario would be interesting in that you'd have neurons belonging to the same memory circuit firing at vastly different rates, each encoding for the amplitude of their respective representation. Ok, so there. Now we have a testable hypothesis. Go prove me wrong :)
Jeremy Biane
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The Future of Science Publishing
Cell Press is currently showing off what it's calling "the article of the future" on it's website. By segregating out different parts of the "traditional" manuscript, linking them together in novel ways, and adding some promising new content, the publishing giant aspires to "take full advantage of online capabilities, allowing readers individualized entry points and routes through the content, while using the latest advances in visualization techniques." Does it work?
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Kinda.
There are a couple great ideas embodied in this project, most notably the graphical abstract (a figure designed to support and crudely sum up the written abstract) and the short audio interview with the author that will explore the rational and implications of the paper's(?) findings. The graphical abstracts available, unfortunately, did not seem to add much insight, and only one interview is available at this time. However, in the right hands and with the right subject matter, both of these ideas could really enhance communication of the article's message.
I'm also a big fan of the comments section. This is something that, if this little OneSci journal ever finds its legs, I think will be an integral part of our publishing model. Of course, we plan on taking it a couple steps further...
Most of the other revisions are really just minor tweaks of already standard content. I'm sure busy researchers will come to appreciate the ability to quickly navigate through different sections and figures of the article, and I actually like the option of viewing a summary of the methods in lieu of an in-depth retelling (because that's exactly what we need as responsible scientists: to gloss over the methods section).
One idea that I was disappointed to not see make it is inclusion of the reviewer's comments. Why are these things never released to the public? I understand that many of the comments may have been addressed by the author and thus are obsolete, but I'd really like to hear what their peers - presumably investigators with a similar background to the author, and who have thoroughly read the article - have to say about this research. It would be so easy to throw these up as attached .pdfs. Why aren't we doing this already?
I definitely applaud Cell for taking strides toward the next generation of science articles, regardless if these early versions seem a bit flat. Now, if we could just get them to quit charging all of us an arm and a leg to view the data we've all funded with our tax dollars (OneSci!).
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Four Color Theorem
 There are no borders of the same color! Say that you’re asked to draw a map of the United States, but are only given four different colored crayons. There is only one rule, you can’t use the same color on two states that share a border. Think you could do it?
The truth is, three colors are adequate for most maps; with an additional fourth color, we can satisfy our rule for anything drawn onto a piece of paper. I didn’t believe it at first, so I tried to crack the theorem. Now I have no more crayons, and lots of abstract artwork.
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In mathematics, the four color theorem, or the four color map theorem, states that given any separation of a plane into contiguous regions, called a map, the regions can be colored using at most four colors so that no two adjacent regions have the same color. Two regions are called adjacent only if they share a border segment, not just a point. Despite this amazing phenomena, the four color theorem is not of particular interest to mapmakers (savages). However, since the original conjecture was proposed in 1852 by Francis Guthrie, mathematicians battled for over a century to arrive at a proven theorem.
Finally, in 1976, while teams of mathematicians were racing to complete a proof for this geometric quagmire, Kenneth Appel and Wolfgang Haken at the University of Illinois, announced that they had proven the theorem. However, some mathematicians felt that they had cheated by using a computer (the cartographers were completely indifferent about the ordeal).
 Using mathematical rules and procedures based on properties of reducible configurations, Appel and Haken found an unavoidable set of reducible configurations, thus proving that a minimal counterexample to the four-color conjecture could not exist. Their proof reduced the infinitude of possible maps to 1,936 reducible configurations (later reduced to 1,476) which had to be checked one by one by computer and took over a thousand hours. This reducibility part of the work was independently double checked with different programs and computers. However, the unavoidability part of the proof was verified in over 400 pages of microfiche, which had to be checked by hand.
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Presendential Lecture - Origins of abstract knowledge: number and geometry
How can you not be intrigued by the subject matter of this talk? Ok, I can see why some may not be, as even the mention of math sends many running for the aisles. But, I find it very intriguing. I mean, this is one of our uniquely human* characteristics that encompasses consciousness, logic, reason and a set of rules that appears be applicable to the entire physical (and perhaps metaphysical) world! So where the hell does one begin to study such a phenomenon as mathematical reasoning???
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In infants, of course. And in monkeys, pigeons, mice and the lowly lab rat. This may seem counterintuitive at first glance, studying high-level human processes in beings with limited cognitive abilities. But Spelke makes a good argument for conducting such studies - and their applicability to higher-level reasoning - by investigating mechanisms found across cultures and species, and which may serve as the foundation of mathematical reasoning.
As a quick example from the talk, studies have shown that human infants (along with nonhuman animals) are able to discriminate gross values. E.g., an infant will spend a longer time looking at a visual display of dots if the quantity of dots is changed (vs. if the contrast, color, or general size of dots is changed) from a previous display. So, it appears there exists an innate mechanism for crudely evaluating sets of objects. And this “nonsymbolic” numerical ability correlates well with symbolic abilities later in life (that is, mathematical abilities such as addition – the process of combining symbols (numbers)). Thus, if we can locate where this nonsymbolic numerical evaluation resides in the brain, we have a good start point of where our abstract abilities might reside.
And this is being done. Admittedly, it’s a large jump from nonsymbolic numerical abilities to abstract mathematical reasoning, but it’s a start. And considering all the mystic phenomena involved in abstraction, I think it’s about all we can expect at this point in time. And once we understand these foundational processes, maybe we can tweak downstream (or would that be upstream?) connections to see which additional structures may contribute to this complex process.
Obviously is a very intricate question, one that I certainly can't do justice. But it is exciting to see the scope of questions we are beginning to actually crack with scientific inquiry.
More to come. But I got a significant other waiting for me back at the hotel, and it's time to get some dinner in this hungry body!
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Computation for everyone!
 How do we learn to perfect our aim? Let’s see…10am on a Sunday...5 hours sleep...Computational lecture...Presenter is British...
Hmmm, lots of factors were adding up to predict a very drab presentation (sorry, Brits. Although that accent sounds quite erudite and impressive, you can be a bit dry). But, holy crap, this lecture rocked! Daniel Wolpert does an excellent job of translating his very technical work to the masses, and gave probably the best synopsis of Baysian theory that I’ve ever heard (though I’m no statistician and can’t comment on it’s accuracy).
His conviction: brains evolved to enable interaction with the environment. Therefore, by understanding movement and how it is controlled, we can basically understand the brain.
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Unfortunately, it’s not as simple as it sounds, as there are many complexities that govern movement selection and control, including multiple degrees of freedom (joints), nonlinear changes (growth of bones and muscle, changes in tension), and noise.
So how does one deal with such confounds and learn to execute controlled, precise movements? By forming an internal prediction of one’s movement and their sensory consequences. Then actual sensory feedback is compared to predicted feedback, and the prediction is updated to minimize the discrepancy between actual and predicted signals (prediction error is minimized).
But how do we predict what movements we should make in the first place? This is where the Baysian rule comes into play. Predictions are based on interplay of previous experiences (memory) and current conditions (sensory data). For example, when determining where you should swing your tennis racket to hit an incoming ball, you rely on the location where the ball usually is located based on previous experience (perhaps your opponent always hits the ball to the boundary line), along with the current position and trajectory of the ball.
Whether this is actually what happens internally when we execute movements and skilled motor patterns is still very much uncertain. But there seems to be mounting evidence suggesting that prediction error feedback is present in at least some (if not all) movements.
Some other (semi-coherent) tidbits:
- Delusions of control – what allows us to label a movement as our own? Perhaps it is when our sensory feedback matches our prediction, making the two largely cancel out. If don’t have this prediction, all we have is sensory feedback and our own movements may seem like they were not initiated by us or out of our control (because error is so large).
- Escalation of force - (tit-for-tat task – apply received level of force to other, which he then applies to you, you back to him, etc, etc). Over 10 trials, force applied increased by 40% over initial level. This is hypothesized to be due to subtraction of prediction, which results in higher level of force in order to match the perceived force.
- Stereotyped movements across individuals (e.g., usually follow straight path). Some trajectories lead to less uncertainty in final position than others, and these paths have been selected for over time.
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(Purge articles)
Behavioral Conditioning via Channel Rhodopsin
If you're in the field of neuroscience and for some crazy reason haven't heard of channel rhodopsin-2 (ChR2) yet, you will. It has got to be the sexiest contemporary technique available for the neuroscientist, and one that will probably earn Karl Deisseroth a trip to Stockholm in the future.
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A relatively new technique, ChR2 allows for tight temporal control of neuronal activation via photostimulation (optically mediated inhibition can be induced via the halorhodopsin channel).
The ability to express ChR2 in specific cell populations via genetic targeting make it far superior to previous methods of neuronal activation. Thus far, ChR2 has mainly been used to study synaptic plasticity and to map functional connections between neurons. However, a new paper in Science is the first to show that ChR2 activation can actually drive behavioral changes in adult mammals. While this result is not all that surprising, it's surely a milestone in the field and a harbinger for many, many studies to come.
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The OneSci Network is an online research community built and operated by active scientists and researchers across various scientific disciplines. OneSci has many specific goals under one broad mission: building a web-portal that encompasses all things considered useful for conducting research.
What we hope to accomplish across a relative time-line:
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Apr 06 2010 9:30 am
test
Apr 06 2010 9:33 am
I have a hunch that cold weather may trigger a virus that has long since invaded it's host, but had remained dormant until the conditions were perfect for reproduction.
Apr 06 2010 1:30 pm
Ha. Interesting you should say that, as I was just discussing with someone how odd it is that after a prolonged bout of stress people tend to get sick. I can think of a couple of times I had a rigorous semester/quarter and the minute i have a break or the quarter ends, bam, illness sets in. But it's not during the period of stress when sickness hits - when I would assume the body is most vulnerable. It's like the body knows you got to keep it together for a bit longer, and moment you get some time off, you finally deal with the virus. I don't think this has anything to do with the weather, but it does exemplify how indirect outside stimuli may trigger or at least influence the onset of illness.