Suppose you're preparing dinner and you realize that you'd like to add some more tomatoes, onions, and mushrooms to the salad that you're making. You have no trouble storing images of these items in your head and locating them in the fridge. Suppose instead that you're eating dinner and you decide that you absolutely must have another bottle of red wine for your guests, but you really want the cote du rhone, and not the boring merlot. Again, you can quite easily go and grab the bottle based simply on its label, but in this latter situation, the amount of visual information: the detail that must be stored for comparison upon reaching your liquor cabinet is far greater.
In both cases, you're employing some form of working visual memory. This form of memory is thought to be highly plastic, short-term storage. It is possible however, that it might have different characteristics. The forms that working memory might take are (1) a set of fixed "slots," each having a discreet capacity or (2) a set of dynamic slots which can be more tailored to the specific use.
The fixed-slots concept is similar to the pictures taken by current digital cameras. The images are always the same number of (mega)pixels, no more, no less. While the dynamic-slots concept is more akin to being able to vary the number of pixels in an image depending on demand. For instance, if I was taking a picture of a pure red wall, I wouldn't need any more than 1 pixel because every part of the image would be identical. However, if you were taking a picture of a Jackson Pollack painting, you might want to combine several digital camera images to get a really accurate rendering of the details held therein, a more flexible allocation of working-memory resources.
The scenarios I've highlighted above don't really serve to answer the question of which form of working memory our brains employ because in both cases, we can imagine either form working just fine (as long as we accept the notion that the complexity of the wine bottle label is within the capacity limits of the fixed-slots). However, a recent paper published in Nature claims to have employed a savvy enough experimental approach to disentangle the two possibilities.
The research, published by Steven J. Luck (who has done quite a bit of excellent work in the field of Visual Neuroscience in general) and a colleague, Weiwei Zhang, is sadly quite brief, having clearly been edited for length by the editors at Nature. In fact, this curtailed version is quite difficult to follow, given the subtlety of the approach that the authors used, but their essential point comes through.
The approach they take is a common one. They measure human performance on a variety of working-memory tasks and attempt to fit these data to models with different assumptions. In this paper, they compare how well the data can be fit to a fixed-slots model versus a dynamic-slots model. Although they conclude that the dynamic-slots model simply doesn't explain the data as well, and they thus discard the notion of flexible resource allocation, one of the final sentences betrays what they must admit: "This model does not completely eliminate the concept of resources, because the slots themselves are a type of resource." In other words, it is possible to allocate multiple slots to the same item object-to-be-held-in-memory. However, it does appear that the slots themselves are of a fixed size.
This result places limits on the possible anatomical underpinnings of working-memory, and makes predictions about how one might expect human beings to perform in other, working-memory tasks. It will be interesting to see if the conclusions that these authors reached will be borne out in future work.
Thursday, May 29, 2008
Tuesday, May 13, 2008
On Male Versus Female (bugs)
Male fruitflies know what their ladies like. They court them with the dulcet tones of their wings themselves. It has been observed that one behavior that males of the species Drosophila Melanogaster can engage in that appears to increase his likelyhood of copulating with a female, is the rhythmic production of sound by wing-vibration; that is if she finds his performance to be sufficiently virtuosic. This behavior is controlled by a central pattern generator (CPG), a group of neurons that, more or less, independently generates the activity corresponding to this motion. Think of this as a sort of reflex, somewhat akin to walking at a constant pace over a completely flat surface in that it is extremely stereotyped and largely automatic.
Nature Magazine
One question this has raised in researchers minds is why this behavior is limited to males. A new study demonstrates that the females have this ability, but they simply suppress it. Apparently the neurons that kickstart the motor of the central pattern generator are simply not active in females. In order to get these cells going, Jai Y. Yu and Barry J. Dickson used a technique involving the manipulation of ion channels with light.
Every neuron's excitability is mediated by the opening and closing of small pores (ion channels) in their membranes which allow charged ions to flow in and out. The regulation of this permeability or conductivity of the membrane determines how excited the neuron can become. Put another way, if a bunch of little doors burst open that allow positively charged ions to flow into the cell, those ions flood in, raising the voltage of the cell, putting it in an excited state. It turns out that certain species of algae have specialized ion channels that are of some use to scientists. Usually ion channels are opened in response to neurotransmitter or enzymatic signals, but these plants have ion channels that are directly "gated" (opened or closed) by impinging light. What Yo & Dickson have done is to exploit a technique in which they borrow these channels and put them into specific cells using molecular genetic techniques, thus allowing them to selectively excite specific ion channels in specific neurons with bursts of luminance.
In this way, they were able to observe that, through the excitation of neurons controlling the CPG, the ladies are able to produce these courtship songs as well, they just didn't know they had it in them.
One question this has raised in researchers minds is why this behavior is limited to males. A new study demonstrates that the females have this ability, but they simply suppress it. Apparently the neurons that kickstart the motor of the central pattern generator are simply not active in females. In order to get these cells going, Jai Y. Yu and Barry J. Dickson used a technique involving the manipulation of ion channels with light.
Every neuron's excitability is mediated by the opening and closing of small pores (ion channels) in their membranes which allow charged ions to flow in and out. The regulation of this permeability or conductivity of the membrane determines how excited the neuron can become. Put another way, if a bunch of little doors burst open that allow positively charged ions to flow into the cell, those ions flood in, raising the voltage of the cell, putting it in an excited state. It turns out that certain species of algae have specialized ion channels that are of some use to scientists. Usually ion channels are opened in response to neurotransmitter or enzymatic signals, but these plants have ion channels that are directly "gated" (opened or closed) by impinging light. What Yo & Dickson have done is to exploit a technique in which they borrow these channels and put them into specific cells using molecular genetic techniques, thus allowing them to selectively excite specific ion channels in specific neurons with bursts of luminance.
In this way, they were able to observe that, through the excitation of neurons controlling the CPG, the ladies are able to produce these courtship songs as well, they just didn't know they had it in them.
Tuesday, April 29, 2008
On sticking out your tongue.
Muscles can only pull, not push, so how is it that you can stick out your tongue? In other words, since we have no muscle outside our mouths to pull our tongues out, the fact that muscles are incapable of pushing seems to imply that there is some sort of mechanism at work in producing this movement which doesn't fit in with our general conception of all other movements (arms, legs, eyes, peristalsis, breathing, et cetera).
i am currently (4/29/08) attending the 18th Annual Neural Control of Movement (NCM) meeting in Naples, Florida, and this tongue question was brought up as a way to remind the attendants that we should be careful not to let dogma affect our thinking too much as such adherence to well-established ideas can prevent us from reaching new ways of understanding and new modes of analysis.
View Larger Map
The simple answer is that the tongue has constant volume, so if you sufficiently contract the muscles in your tongue laterally (from molar to molar), the tongue must increase its volume longitudinally (from throat to lips). Notice also that to really stick your tongue out, you must protrude your mandible significantly.
In any case, it was a perhaps frivolous but highly stimulating and poignant aside in a meeting otherwise devoted to the serious analysis of experimental data, thus kicking things off in a congenial tone.
Wednesday, April 23, 2008
Quote
Personally, I see no contest between a belief in the existence of a deity and the study of science. Indeed, many of our greatest scientists have been strongly religious (Aristotle, Einstein, Newton, et cetera). Nonetheless, the world-wide conflict between the two rages on. Here I present an excerpt from an essay published in Nature about the very birth of science and it's seemingly automatic perception as challenging religious faith.
"[William of Conches] argued that natural phenomena arise from forces that, although created by God, act under their own agency. William insisted, echoing Plato, that the divine system of nature is coherent and consistent, and therefore comprehensible: if we ask questions of nature, we can expect to get answers, and to be able to understand them.
That is a necessary belief for one even to imagine conducting science. If everything is subject to the whim of God, there is no guarantee that a phenomenon will happen tomorrow as it does today, therefore there is then no point in seeking any consistency in nature. But William of Conches could not countenance a Creator who was constantly intervening in the world. He saw the Universe as a divinely wrought mechanism: God simply set the wheels in motion. It is in the twelfth century that the first references to the Universe as machina begin to appear.
Some conservative theologians denounced this attempt to develop a Christian platonic natural philosophy. They felt that taking too strong an interest in nature as a physical entity was tantamount to second-guessing God's plans. As everything was surely determined moment to moment by the will of God, it was futile and impious, they believed, to seek anything akin to what we now regard as physical law. The quest for laws of nature was also condemned because it seemed to limit God's omnipotence. As the eleventh-century Italian cleric Peter Damian insisted, one could not know anything for certain, as God could alter it all in an instant.
...
Opposition to medieval rationalism was motivated in part by valid concerns about the dangers of bringing science into scripture. When, for example, William of Conches was denounced for seeking physical explanations for the creation of Eve from one of Adam's ribs, conservatives were right to voice dismay at this apparent transformation of the Bible into a work of science. Read as a kind of moral mythology, holy books may have some social value. Deeming them sources of natural facts must lead to the absurdities of today's creationism.
By making God a natural phenomenon, the medieval rationalists turned Him into an explicatory contingency for which there has since seemed ever less need. By degrees, such secular learning was found to have so much explanatory power that it rivalled, rather than rationalized, theology itself. The consequent rift between faith and reason has now left traditional religions so compromised they are susceptible to displacement by more naive and dogmatic varieties."
from Triumph of the medieval mind by Philip Ball (Nature 452, 816-818 (17 April 2008) | doi:10.1038/452816a)
Wednesday, April 16, 2008
On Looking For Things That May Not Be There
On Monday (4/14/08) I had occasion to attend an informal talk on dark matter and dark energy at the Picnic Café given by Alberto Nicolis as part of a continuing series called Café Science in collaboration with Columbia University. I think this sort of casual interaction between scientist and the general public is all to rare, and I was heartened by the lively discussion that resulted between the speaker and the capacity crowd of about 40 individuals spanning ages from 13 to 80. The talk itself lasted about 30 minutes, with Alberto taking sips of white wine from his stance in the middle of the room.
His talk was intended to provide, in broad strokes, a lay understanding of those mysterious terms I mentioned above, dark matter and dark energy. In general, the reason for their recent explosion of use in scientific literature and news media in general is a matter of length scales.
It goes like this: we have two ways of talking about gravity: Newton’s law of universal gravitation, and Einstein’s theory of general relativity. Within out solar system, they are essentially indistinguishable in they are both quite capable of explaining the orbits of the planets (although general relativitistic calculations are required for use of the global positioning system, GPS). When astronomers observe galaxies - where the effects of gravity occur over much larger distances - however, things are different.
Allow me a brief dalliance for the purpose of pedagogy: it is possible to estimate the mass of a galaxy by the number of stars and planets it contains. Even the presence (and indirectly the mass) of black holes can be inferred by watching more distant stars pass behind and become occluded by them, in this way we can calculate the approximate mass of all the matter that we can see. This is where we run into a problem. Based on these calculations, the stars in these galaxies are moving far too fast, there is not enough mass to explain their observed velocities. By rights, if these stars are moving this fast, their centrifugal acceleration should shoot them away from their galactic homes. This realization has led to the inference that there must be some additional mass in the galaxy that we cannot see; that simply doesn’t interact with light as all known matter does, to create enough force to bind these stellar conglomerations together. This is dark matter.
A graphical representation of dark matter
Another astronomical observation on a still larger length scale leads to further worries. It is believed that the universe is expanding because of the observation that individual galaxies appear to be moving away from one other. This wouldn't be troubling at all if their relative velocities were constant, it could be explained as a remnant of the outward linear momentum created by the big bang, but it isn’t. In fact the rate of expansion seems to be increasing with time. Because all matter creates attractive gravitational force, one cannot here again invoke dark matter, this would only exacerbate the issue. To deal with this conundrum, we must instead postulate some other weird stuff that creates a repulsive gravitational force. This is where dark energy comes in. Because one well known feature of Einstein’s general relativity is the ultimate equivalence of matter and energy (E=mc2), it accommodates the existence of some form of energy that generates the requisite force to produce the acceleration of the universe.
These explanations are fairly tidy in the sense that they work well at patching up existing theories. However, in talking with Dr. Nicolis after the conclusion of his remarks and the Q&A session, a slightly different picture arose. I was questioning him about the recently raised possibility that the assumption of a homogeneous distribution of dark energy requires substantially different corrections to general relativity than some other, more exotic distributions might1. His sensible response to this was to point out that we have no reason, theoretical or otherwise, to favor any particular distribution of dark energy over any other (unlike dark matter, which must take on a very specific distribution in order for it to have the appropriate effect). In fact, his own work on gravitation goes even further, abandoning the concepts of dark matter and dark energy entirely in favor of a more fundamental reformulation of the laws of gravity which we hold so dear. This may sound radical, but it actually bears a resemblance to a similar sort of decision made by Einstein early in this century.
I am referring here to Einsteins rejection of the concept of æther. This enigmatic substance was originally proposed by Isaac Newton in order to explain what we now know to be the effects of gravity on light. He observed that light from distant sources was diffracted or bent in proximity to certain heavenly bodies, and proposed that there must be some all pervasive stuff collecting near heavy objects which was responsible for this effect. Further, and much later, it was suggested that the æther was needed to support the propagation of electromagnetic waves through space.
The short version of the story is that while some were performing painstaking and extremely sensitive experiments designed to distinguish between various theories of æther, Einstein was quietly developing his theory of special relativity, which did away with the need for such machinations entirely.
It is anybody's guess what the outcome of all this dark matter & energy business will be, but one thing is sure, the most interesting science is born out of times like these; eras in which empirical observations challenge experimentalists and theoreticians alike to tinker with and explain things they don't understand.
References:
1. Ellis, G. (2008) Cosmology: Patchy solutions. Nature 452, 158-161 | doi:10.1038/452158a;
Another astronomical observation on a still larger length scale leads to further worries. It is believed that the universe is expanding because of the observation that individual galaxies appear to be moving away from one other. This wouldn't be troubling at all if their relative velocities were constant, it could be explained as a remnant of the outward linear momentum created by the big bang, but it isn’t. In fact the rate of expansion seems to be increasing with time. Because all matter creates attractive gravitational force, one cannot here again invoke dark matter, this would only exacerbate the issue. To deal with this conundrum, we must instead postulate some other weird stuff that creates a repulsive gravitational force. This is where dark energy comes in. Because one well known feature of Einstein’s general relativity is the ultimate equivalence of matter and energy (E=mc2), it accommodates the existence of some form of energy that generates the requisite force to produce the acceleration of the universe.
These explanations are fairly tidy in the sense that they work well at patching up existing theories. However, in talking with Dr. Nicolis after the conclusion of his remarks and the Q&A session, a slightly different picture arose. I was questioning him about the recently raised possibility that the assumption of a homogeneous distribution of dark energy requires substantially different corrections to general relativity than some other, more exotic distributions might1. His sensible response to this was to point out that we have no reason, theoretical or otherwise, to favor any particular distribution of dark energy over any other (unlike dark matter, which must take on a very specific distribution in order for it to have the appropriate effect). In fact, his own work on gravitation goes even further, abandoning the concepts of dark matter and dark energy entirely in favor of a more fundamental reformulation of the laws of gravity which we hold so dear. This may sound radical, but it actually bears a resemblance to a similar sort of decision made by Einstein early in this century.
I am referring here to Einsteins rejection of the concept of æther. This enigmatic substance was originally proposed by Isaac Newton in order to explain what we now know to be the effects of gravity on light. He observed that light from distant sources was diffracted or bent in proximity to certain heavenly bodies, and proposed that there must be some all pervasive stuff collecting near heavy objects which was responsible for this effect. Further, and much later, it was suggested that the æther was needed to support the propagation of electromagnetic waves through space.
The short version of the story is that while some were performing painstaking and extremely sensitive experiments designed to distinguish between various theories of æther, Einstein was quietly developing his theory of special relativity, which did away with the need for such machinations entirely.
It is anybody's guess what the outcome of all this dark matter & energy business will be, but one thing is sure, the most interesting science is born out of times like these; eras in which empirical observations challenge experimentalists and theoreticians alike to tinker with and explain things they don't understand.
References:
1. Ellis, G. (2008) Cosmology: Patchy solutions. Nature 452, 158-161 | doi:10.1038/452158a;
On STDP from Behavior
I've written several times about Spike-Timing-Dependent-Plasticity (STDP), one method by which the individual neurons in the mammalian brain learn; changing their responses to the signals sent from other neurons.
It is believed that STDP is a major route of such learning, both during development and in the adult animal; for instance potentially underlying the associative conditioning famously demonstrated by Pavlov. Indeed, it is just this sort of patterned external sensory stimulus (bell then food) that represents a candidate for learning through STDP. However, connecting the presence of structured environmental variables and underlying brain changes has proven a difficult experimental challenge.
A recent piece of research has achieved just such a feat in the optic tectum of a non-mammal, the developing frog Xenopus laevis1.
I found the figure above to be the most intriguing result from the paper sumarrizing these experiments, published in Nature Neuroscience. The image represents the finding that if the tadpoles are exposed to repetitive flashes of light with a specific time difference between them (top), the neurons in their optic tecta respond by adjusting the latency (time from stimulus to response) of their spike-reactions to single, isolated flashes (middle: neural activity, bottom: histograms of spike latencies).
I am encouraged by this work because it bridges what is currently a rather formidable gap. That between understanding the brain at the level of single neurons and the behavior of an animal as a whole.
References:
1. Pratt, KG, Dong, W & Aizenman, CD (2008) Development and spike timing–dependent plasticity of recurrent excitation in the Xenopus optic tectum Nature Neuroscience 11, 467-475 | doi:10.1038/nn2076
Friday, April 11, 2008
On Hippocampal Memory
The hippocampus is the area of the mammalian brain most closely associated with memory, particularly spatial memory, meaning internal maps. It is common for individuals with hippocampal lesions (and surrounding related regions) to have amnesia, as in the famous case of H.M., and more recently E.P. Also, it has been shown that London cab-drivers, who presumably require large internal maps for navigation, have enlarged hippocampi1. However, it is has become clear that the hippocampus isn't required for all forms of memory. A set of looming questions, then, is: where memories are stored, how are they formed, and what role various structures in the brain play in these activities.
I attended a seminar (3/26/08) given by Larry Squire, who has been studying the role of the hippocampus in memory formation, retrieval and storage for quite some time. He summarized results comparing normal patients to those with hippocampal lesions. In general, it seems as though this structure is required for the formation of certain types of new memories only, not for recall or storage, since the lesioned patients had no trouble remembering thigns from their past (including how to navigate their childhood neighborhoods). However, the truly fascinating result he presented was from an experiment designed to reveal what role the hippocampus plays in building new memories.
The figure above summarizes the data gathered from normal and hippocampal-lesion patients during a memory task. The task was quite straightforward, subjects were presented with 8 pairs of objects, one pair at a time, in which one of the pair was always "correct." In a given trial, the subject was presented with a pair, and chose one by reaching out and picking it up. On the underside of each object was a sticker that either read "correct," or "incorrect." As represented in the left panel of the figure above, normal humans were able to reach perfect performance in this task by repeated presentation of these pairs3. In addition, these subjects were easily able to cope with a variation on the task. All 16 objects were presented at once, and the subject was asked to separate out the correct from the incorrect items (indicated by the grey bar to the right of the trace).
The hippocampal-lesion patients, however, showed dramatically different results. They required 12 times as much training, didn't get up to the same level of performance as the normal subjects, and were unable to perform the task-variant. This is to say nothing of the fact that they didn't recall having ever attempting the task before when queried on the 2nd through 36th sessions.
It is fascinating that these patients were able to learn this task at all, and it is clear that they are using some completely different strategy from the normal subjects (one which relies heavily on the paired-object context, as revealed by their confusion at being presented with all the objects at once). There are several questions that are raised by this research. First, if there is an alternative pathway for learning such tasks, how does the brain choose to use the hippocampal path to record a particular type of memory? One might suggest that the brain uses the hippocampus for all memories unless it isn't useable as in these patients, but we know that many types of motor learning, learning to play the piano for instance, do not require the hippocampus. A further set of interrelated questions are where and how the memory is being stored, and how these differ from those patients with intact hippocampi.
This type of research shows us definitively that we have the capacity for different types of memory, and that we have a ways to go in understanding how and where it operates.
Notes & References:
1. Maguire EA, Frackowiak RS, Frith CD. (1997) Recalling routes around london: activation of the right hippocampus in taxi drivers. J Neurosci; 17(18):7103-10.
2. Bayley PJ, Frascino JC, Squire LR. (2005) Robust habit learning in the absence of awareness and independent of the medial temporal lobe. Nature; 436(7050):550-3.
3. A "session" in this experiment consisted of 40 trials, 5 presentations of each of the 8 pairs of objects.
Friday, April 4, 2008
Wellcome Images 2008
The Wellcome Trust is the largest charity in the UK. They are well described by this quote from thier website: "We fund innovative biomedical research, in the UK and internationally, spending around £650 million each year to support the brightest scientists with the best ideas." Further, "Wellcome Images is one of the Wellcome Library's major visual collections. Part of Wellcome Collection, a major new £30 million public venue developed by the Wellcome Trust, the Library has over 750 000 books and journals, an extensive range of manuscripts, archives and films, and more than 250 000 paintings, prints and drawings." And, "The annual Wellcome Images awards (previously known as Biomedical Images Awards) reward contributors for their outstanding work and winners are chosen by a panel of experts. The resulting public exhibitions are always extremely popular and receive widespread aclaim."
Above is one image from the 2008 awards collection, you can view the rest here, and many other images from their library here.
This sort of thing has actually become de rigeur for Nature & Science/NSF who give our awards on a slightly larger scope, including interactive media, and breaking down imagery into real and virtual categories.
Wednesday, March 19, 2008
On Reading Minds
Who hasn't had the desire to see through another's eyes? Some researchers at Berkeley think they've taken the first steps towards achieving such a goal.
Jack L. Gallant and his lab-mates have managed the feat of decoding human fMRI measurements in such a way that they can infer the image that generated the recorded neuronal activity1. fMRI as a technique assesses brain excitation indirectly, through blood-flow. The degree of excitation is clearly in some way related to the BOLD (Blood-oxygen-level dependent) signal obtained, but it is a bit crude in the sense that it isn't very spatially or temporally precise2. The data can pinpoint activity to a few square millimeters, and within a window of about 6 seconds.
The paper detailing their results, appearing in Nature, describes how this remarkable trick was accomplished. First, the researchers consulted fMRI signals from subjects viewing a wide variety of natural images. They correlated this information with the pixels in the pictures themselves, and this allowed them to construct a model which predicted the pattern of blood-flow one might observe with fMRI in response to an arbitrary image. Once this was done, they essentially turned the model on it's head so that they could ascertain the viewed image from the fMRI data. In fact, at present it's quite a brute force approach that requires that the scientist have a set of images which are fed into the model to generate synthetic fMRI data to compare with the measured signals. However, it is possible that models of this form will eventually be sophisticated enough to avoid this.
If these techniques could, for example, be extended to other forms of mental reckoning, we might some day be able to see into the thoughts of those who are unable to communicate. Regardless of the practical applications, and however far from sneaking a peek the richly textured visual experience we each have, this type of savvy utilization of data and modeling techniques is exciting because it tickles the basic desire we all have to know another's being.
Notes/References:
1. Kay KN, Naselaris T, Prenger RJ, Gallant JL. (2008) Identifying natural images from human brain activity. Nature, 452;352-355
2. Other technologies sacrifice the large volume of brain space that fMRI can cover for spatial precision (over 10000x better, single cells) and temporal precision (over 100000 times better, though that much is not necessary).
Tuesday, March 18, 2008
On Emergent Causation
I recently read a one-page book review of a text whose subject matter strides through consciousness, free will, and emergence1. The review, by Todd S. Ganson, focuses on how the book, Did My Neurons Make Me Do it?, contends with a classic problem in neuroscience and the philosophy of mind: how is it possible to attribute mental states exclusively to the brain while avoiding a completely determined (lacking in free will) existence2?
Ever since Descarte pointed out the problems with dualism (a separation of the material and the mental), philosophers have been hard at work to find a middle ground between eliminating the mental and resorting to the supernatural. On the one hand, subjective conscious experiences cannot be denied, and it thus seems foolish to claim that they do not exist. However, there is absolutely no hint of a description as to how mentality might be caused by our biological apparatus, and it is thus somewhat attractive to assert some other author to our cognitive being, leading some to invoke the supernatural.
It has been suggested that one way to illustrate the manufacture of subjective experience is describe it as emergent. An example of an emergent property that I find to be particularly useful is the liquidity of water. A single molecule of H2O is not a fluid; rather the quality of being liquid is predicated on the interactions between many molecules. It is a property that emerges from the collective. Another example might be sand dunes: the patterns present in large quantities of grains are a feature of their concert, not guaranteed by the individuals.
Emergence has been very helpful to some because it paints a picture in which consciousness is not a priori predictable from the actions of single neurons, and yet retains a tangible quality. It doesn’t explain how the cerebrum causes consciousness but it does assert a mode in which consciousness might stymie our current scientific attempts to understand it based on the actions of single brain cells.
This book takes the utility of emergence one step further by putting forth the idea that emergence might help us reconcile our personal feeling of responsibility for our actions with our materially deterministic substrates of brain. The idea is that the complex system that is our emergent consciousness "can causally influence what bottom-level events occur by shaping the conditions that trigger these events.2"
An apt analogy here again is the sand dune. Its over-all shape determines how the individual grains interact with such forces as the wind. If it forms a flatter dune, it will be less susceptible to the whims of the wind while a tall structure will be more fragile. In this sense, the collective behavior can influence the actions of the individuals which make up the whole.
As mentioned previously, an alternative to searching for ways in which our seemingly ephemeral consciousness can effect the matter in our heads, we can adopt the view that free-will is an illusion; another mechanism of our brains that keeps us happy in the delusion that we're in charge of our own actions.
In any case, the suggestion concerning emergent causation may not explain anything in specific, but it does help to frame an alternative way to think about the relationship between that vexing triad I mentioned at the top, free will, consciousness and emergence.
Notes:
1. The interested reader might click here for RadioLab's excellent show on the subject of emergence.
2. Ganson, T.S. (2008) Finding Freedom Through Complexity. Science; 319:104
Monday, March 10, 2008
On Combinatorial Construction of Language
It's rather fortuitous that the article I'm about to discuss popped up right after my last post, a discussion of how critters' need for audio-specific brain adaptations depends in part on the complexity of their vocalizations.
The piece of work I'm referring to is a relatively brief description of research on the putty-nosed monkey (figure 1). The finding is that these animals use two types of calls, so-called "pyows" and "hacks" in a combinatorial way: they string together these two words (if you will) to form longer phrases1.
The authors demonstrate that different combinations (pyow hack pyow pyow, vs. hack hack pyow pyow, or something to this effect) can code for distinct predators (leopard vs. hawk). Further, they indicate that novel combinations of the sounds elicit different group behaviors, and that the animals behave differently when the certain calls come from a within-group male rather than a stranger.
This smacks of the beginnings of language to me, in part because one feature of our sentential grammar is iterative construction. Noam Chomsky and others pointed out this method of building up of new sentences with new meaning by tacking extra bits onto the old ones.
References:
1. Arnolda, K & Zuberbühler, K (2008) Meaningful call combinations in a non-human primate. Curr. Biol; 18(5):R202-R203
Friday, March 7, 2008
On Recognizing Conspecifics
Communication with members of ones own species is extremely important for social animals. Non-verbal messages can signal socially significant events such as the presence of a predator or the movement of the group. It is therefore no great surprise that some recent research has found monkey brain areas specialized for recognizing conspecifics1. This is a sensible sensory strategy, one which ensures that individuals are able to distinguish between the various growls and caws that they might be privy to, and pluck out the ones most relevant to their continued survival.
In animals where vocalizations transcend the guttural, even more specialization has been unearthed (unbrained?) in the skull. Work on Zebra finches has demonstrated that there are neurons which are active specifically during the production of an individuals song (they have, after all, only one in a lifetime) or while the animal hears his song being played back. Again we can confidently say that this type of anatomical customization is full of utility since it allows the animal to monitor and potentially modulate its learning. In fact it would be difficult to imagine the learning process without this type of helpful structure.
References:
1. Petkov CI, Kayser C, Steudel T, Whittingstall K, Augath M, Logothetis NK. (2008) A voice region in the monkey brain. Nat Neurosci. 11(3):367-74.
2. Prather JF, Peters S, Nowicki S, Mooney R. (2008) Precise auditory-vocal mirroring in neurons for learned vocal communication. Nature. 451(7176):305-10.
Sunday, March 2, 2008
On Learning and Time-Scales
On Thursday (2/28/08), I attended a lecture, part of the Neurological Institute at Columbia University’s continuing Seminar series. The talk, titled Activity-Induced Modifications of Neural Circuits was given by Moo Ming Poo, perhaps the most active researcher in this fascinating sub-field, from UC Berkeley. The lecture hall, which has seats for perhaps 80 that are never filled at these events, was packed. Standing room was all that was available, with people spilling into the side aisles and a few intrepid souls (the principle investigator in the lab I work in amongst them) seating themselves in the center aisle as well. Eric R. Kandel, James H. Schwartz, and Thomas M. Jessel (the authors of the most widely used undergraduate and graduate textbook on Neuroscience and all professors at Columbia) were in attendance, as well as countless other researchers and graduate students like myself.
The talk was probably so well attended in part because of Dr. Poo’s notoriety, but also because his work is of some universal intrigue, having relevance in all brain areas and a diverse set research programs.
The title of the seminar refers to the property that individual neurons display in changing the strength of the connections (synapses) between each other in a way that depends on their relative activities. Specifically, if neuron A sends a connection to neuron B, the latter cell needs a way to update the importance of A’s input. Neurons communicate by sending aroundspikes, large pulses of voltage, and it is generally not the case (in the mammalian brain) that a single presynaptic neuron (A) can cause a postsynaptic neuron (B) to fire (spike), rather some several hundred or thousand presynaptic neurons must spike at almost the same time to cause a postsynaptic neuron to fire. I say this because it makes clear the subtle specificity that is needed in synaptic modification, to pick out which of these many inputs have useful information for the cell.
The sensible mechanism, called spike-timing dependent plasticity (STDP), works by up-weighting or strengthening synapses from presynaptic neurons that spiked a short time (20 milliseconds) before the postsynaptic neuron, and down-weighting those in which the presynaptic spike came during a short period after the postsynaptic spike. The large-scale analogy (mentioned by Dr. Poo in his talk) is that of Pavlov’s dog: the canine learned to associate the bell with the meat when the sound preceded the reward, but not when the order was reversed.
In his excellent lecture, Dr. Poo presented some convincing results concerning the molecular mechanisms that might be at work on a microscopic level to achieve this effect, but he concluded with a different and stimulating point.
He presented data from experiments that had been conducted by a post-doctoral researcher in his lab, German Sumbre, using Zebrafish. The results indicated that even very young members of this species are able to learn a predicable series of 60 or so light pulses, delivered every 0.5, 1 or 4 seconds, as indicated by their immaculately timed execution of an escape response called a tail-flick for two or so extra intervals beyond the conclusion of series, right when the pulses would have arrived.
That the fish were able to do this is not incredibly surprising, many animals display this type of predictive behavior. However, it is entirely mysterious how this might be happening at a neuronal level. Dr. Poo has made magnificent progress in understanding how learning proceeds on very short time-scales (10s – 100s of milliseconds), but it is still quite unclear how learning of longer-period phenomena might be achieved by the nervous system. In fact, Dr. Poo appealed to the audience, saying: “If anybody has any ideas how this might be studied, I am anxious to hear them.”
Mirroring the urgency apparent in Dr. Poo's request, there was a paper published recently on this very topic showing that amoeba are capable of just this sort of learning of intervals1. No doubt this will be a hot topic of research in the near future. Further, this sort of example shows us both how far we’ve come in understanding brains, and the chasms yet to be bridged in moving forward.
References
1. Saigusa T, Tero A, Nakagaki T, Kuramoto Y. (2008) Amoebae anticipate periodic events. Phys Rev Lett. 100(1):018101
Saturday, March 1, 2008
On Sexual Selection
This gentlebird has developed a very neat ability. He can produce a high pitched chirp through the vibrations of its tail-feathers in a high speed dive. Christopher James Clark and Teresa J. Feo at Berkeley did some careful analysis to show that this was in fact not a vocalization and they speculate that this act of sonic production is most likely useful for attracting females1.
Generally, I think of evolution as being driven by fitness, but this case reminded me of how critically important sexual selection is in determining special stability and the retention of genetic traits. Peacocks present a more well-known example, their elaborate tail feathers being of a similarly minimal utility in any other (non-reproductive) aspect of their lives.
In sexual creatures, its actually fairly obvious that this would be the case, since maximal reproduction leads to maximal offspring. It is thus important to remember that what drives evolution is not simply survival, but survival and reproduction.
References
1. Clark CJ, Feo TJ. (2008) The Anna's hummingbird chirps with its tail: a new mechanism of sonation in birds. Proc Biol Sci. 22;275(1637):955-62.
Thursday, February 28, 2008
On Walking
When I walk, it feels like a unified action. I mean this in contrast to something like climbing a ladder whence I am extremely aware of the left-right-left-right nature of the commands I must send to my limbs in order to achieve my ascent.
I was thus quite surprised to learn from a paper appearing in journal last year that there appear to be completely separate control mechanisms for operating each of one’s legs while walking. I had (somewhat naively) assumed that my coherent ambulatory experience implied a single underlying motor-program or brain-circuit.
The authors of this paper showed that human beings have no trouble at all walking on a pair of treadmills (one for the left leg and one for the right) moving in opposite directions. Further, they had people abruptly switch between various combinations of directions (forward and forward, backward and forward, forward and backward, backward and backward) and speeds, with short periods (5-10 minutes) of readjustment. Because we essentially never encounter these types of situations in out every day experience, and yet adapt to them very rapidly, the authors concluded (sensibly, I think) that we must have distinct regulators of leg movement for walking.
A figure from the paper mentioned above
On some level this is unsurprising, clearly it is possible to move one leg independently from the other. However my assumption above is not completely without basis; it was Charles Sherrington who won a nobel prize for the discovery that cats can execute a walking motion using only the neurons in their spinal cord. In a somewhat troublesome to consider series of experiments, he demonstrated that spinal-cord severed cats (no communication between spinal cord and brain) whose weight is mostly supported while their feet rest on a moving treadmill can go for a rudimentary stroll. These cats were in effect walking reflexively.
What is intriguing about this whole situation is the degree to which our consciousness has access to what is going on in our neurons. Obviously we don’t have to determine individual muscle tensions or relationships between contraction and flexion when we move, instead we have ideas like “kick the ball” or “walk up the stairs” and our subconscious translates those into motor output. But could it be possible to gain access to that information? Highly trained athletes and those who must be extremely in tune with their bodies probably have a much greater degree of control, but they probably never feel a motor neuron’s spike rate change as they command it to apply more force. Thus, at some level, we simply do not have conscious control of our bodies.
This is a bit unsettling, but it is also exciting because it means that we really must reframe the way we think about the relationship between minds and brains. At least, I must not take for granted that my consciousness is a total reflection of what is happening in my brain.
References
1. Choi JT, Bastian AJ. (2007) Adaptation reveals independent control networks for human walking. Nat Neurosci. 10(8):1055-62.
I was thus quite surprised to learn from a paper appearing in journal last year that there appear to be completely separate control mechanisms for operating each of one’s legs while walking. I had (somewhat naively) assumed that my coherent ambulatory experience implied a single underlying motor-program or brain-circuit.
The authors of this paper showed that human beings have no trouble at all walking on a pair of treadmills (one for the left leg and one for the right) moving in opposite directions. Further, they had people abruptly switch between various combinations of directions (forward and forward, backward and forward, forward and backward, backward and backward) and speeds, with short periods (5-10 minutes) of readjustment. Because we essentially never encounter these types of situations in out every day experience, and yet adapt to them very rapidly, the authors concluded (sensibly, I think) that we must have distinct regulators of leg movement for walking.
On some level this is unsurprising, clearly it is possible to move one leg independently from the other. However my assumption above is not completely without basis; it was Charles Sherrington who won a nobel prize for the discovery that cats can execute a walking motion using only the neurons in their spinal cord. In a somewhat troublesome to consider series of experiments, he demonstrated that spinal-cord severed cats (no communication between spinal cord and brain) whose weight is mostly supported while their feet rest on a moving treadmill can go for a rudimentary stroll. These cats were in effect walking reflexively.
What is intriguing about this whole situation is the degree to which our consciousness has access to what is going on in our neurons. Obviously we don’t have to determine individual muscle tensions or relationships between contraction and flexion when we move, instead we have ideas like “kick the ball” or “walk up the stairs” and our subconscious translates those into motor output. But could it be possible to gain access to that information? Highly trained athletes and those who must be extremely in tune with their bodies probably have a much greater degree of control, but they probably never feel a motor neuron’s spike rate change as they command it to apply more force. Thus, at some level, we simply do not have conscious control of our bodies.
This is a bit unsettling, but it is also exciting because it means that we really must reframe the way we think about the relationship between minds and brains. At least, I must not take for granted that my consciousness is a total reflection of what is happening in my brain.
References
1. Choi JT, Bastian AJ. (2007) Adaptation reveals independent control networks for human walking. Nat Neurosci. 10(8):1055-62.
Wednesday, February 27, 2008
On Active Perception
Perception is more than the passive response to stimuli. When you focus your visual attention on something, it looks different. This is not about gaze, the orientation of the most sensitive part of your retina (the fovea), here I mean that somewhat intangible ability we have to devote our mental processing to an object in, or area of visual space without directly regarding it. Experimental data in the form of human verbal reports and the activity of single cells in the brains of monkeys demonstrate that this is quite concrete: visual attention makes you and the cells in your brain better able to distinguish a variety of properties such as color, the angle of lines and small distances1.
This viewpoint, observation as both active and reflexive, highlights a dichotomy present in debates concerning brain function in general. That is the distinction between seeing such neural exciation as occurring “bottom-up,” driven by the responses to incoming stimuli, versus “top-down,” controlled by higher level cognitive (dare I say conscious) processes.
Figure 1
For example, there is a Dalmatian (somewhat) hidden in figure 1. Without your knowledge of its presence, it is perhaps more natural to simply see a collection of dots, but once you've found that dog, its impossible to miss. This demonstrates that your perception of the object is not purely bottom-up. The image impinging on your photoreceptors alone doesn't necessarily lead to the experience of seeing the object. Other examples include seeing faces in clouds, a result of our overactive face recognition areas, or hearing words in the sounds produced by a gaggle of geese. These are both top-down examples, your existing perceptive mechanisms imposing themselves on the incoming information.
Charles Gilbert, a professor of Neurobiology at Rockefeller University, has been researching visual perception by recording from single neurons in the brains of monkeys for quite some time. A recent paper from his laboratory was aimed at quantifying the role of attention in the perception and cortical processing of a specific visual stimulus: long contours made of small individual line segments3. Figure 2 contains examples of these contours made from more (A) to fewer (C) subsegments .
Figure 2
There are single neurons in your visual cortex (area V2) that will become active when exposed to these larger, constructed contours in certain parts of the visual field. This response is built up from those of cells (in area V1) that are excited by exposure to small, continuous lines in particular places like the ones making up the larger edges above. The reactions of these cells are in turn shaped from the combination of many small, pixel-like bits of information, coming from the retina. This is the hierarchy of the visual system, the responses of neurons that represent progressively more complex objects are formed from earlier, simpler patterns, until we end up finally with neurons that respond best to images of your grandmother, or Bill Clinton, or Jennifer Aniston2.
This hierarchy is important to the theme of top-down versus bottom-up because it informs us as to what is at the top and what is at the bottom. It also allows us to construct a simple example that we can apply to more complicated cases.
Figure 3
The shapes in figure 3 are called Kanisza figures, but many scientists do refer to them as the pac-men that they bear more than a passing resemblance to. It is hard to ignore the triangle that seems to be formed by this particular configuration of polygons, despite the fact that each edge is missing a large segment. What’s happening here is that there are enough cells in V1 turned on, collinearly, by the partial edge of the invisible triangle to excite the V2 cell that would respond to a whole triangle edge in the same location. This is not the whole story however, it turns out that in addition to the bottom-up connections mediating the hierarchy I described before, there are also extensive feedback projections from higher areas like V2 to lower ones like V1. Thus, when the cell in V2 relays information up to higher areas that there appears to be a line spanning two of the Kanisza figures, it also informs all of the V1 cells that might be making up that line, including both the ones which are in this case actually being stimulated, and the interstitial ones where there is no edge to detect. The cells not receiving any actual visual stimulus are activated, to a lesser extent than they might be, by the feedback or top-down signal.
This then is the paradigm for top-down perception. Something like this is most likely happening in the Dalmatian example as well, with some high-level neuron that responds to dogs being activated and sending feedback signals down to all of the neurons that would normally activate the percept, facilitating the “segmentation” of the dog from the background.
What Gilbert and his colleagues did in order to more thoroughly understand this process was to engage a monkey in a task related to the perception of these incomplete contours while measuring the responses of the neurons in their area V1.
The monkey was presented with two images like the ones in figure 2 simultaneously, one in which some (1-9) of the line segments were oriented to form a contour, and one in which their angles were random. It’s task was to simply look at the one with the contour. Maybe because of something intrinsic to their visual system, or maybe just because the monkey didn't understand what the experimenters wanted them to do, they required extensive training before they became proficient at this game.
Figure 4
Before becoming experts at performing this chore, the cells in V1 which respond to the smaller constituent segments responded with a transient increase in their activity (figure 4, left). However, once they had become skilled at this task, the transient was followed by a prolonged bout of activity whose amplitude was proportional to the number of colinear segments making up the larger contour (figure 4, right). Intriguingly, even after the training, if the monkeys were anesthetized and exposed to the images the responses again showed only a transient increase, and no difference between the number of line segments making up the contour. What this suggests is that the engaged, top-down process of performing the task and attending to the stimuli is what generated the difference in the responses, thus active perception.
The idea of active perception brings to mind a question: what aspects of sensory experience are subject to this kind of cognitive control, and what are its limitations? Extreme cases are somewhat helpful: you can't see a circle when presented with a square, although every circle you've ever seen on a computer monitor or television is simply a lot of pixels, and thus not really a circle. This sort of fuzziness is certainly present in somatosensation (touch). An example, if I arrange a situation where your hand is hidden, but there is a rubber hand approximately where yours might be, I can (with a bit of "training") evoke a somatosensory experience in you by touching the rubber hand. Multiple pairings of poking your hidden hand while simultaneously having you watch me poke the rubber stand-in will lead to the feeling that any touch of the rubber hand is a touch of your hand. Further, I have certainly had the experience of enjoying the taste of something before knowing what it was, implying that the mere knowledge of the thing to be tasted can modify the experience of tasting it.
There must be some evolutionary/developmental aspect to all of this. At least in the vision example, if our evolution didn't provide us with feedback connections the likes of which I described above, there would be no anatomy to mediate the top-down control. Similarly, if our development didn't equip our visual systems with automatic detection systems for faces and lines and circles, there would be no high-level percept to feed-back down to lower systems, against which our brains might try and favorably compare incoming data.
The question then becomes, to what extent is this effect mediated by sheer brain circuitry and to what extent by the nebulous mystery that is conscious experience? I would have to argue that our brain circuitry is the only basis for our conscious experience, and thus any effect that we might attribute nonspecifically to our mental being represents our lack of knowledge about the connectivity in our skulls. However, my mother taught me that it is a great thing to be wrong, because that means you've got something to learn. So in any case, I look forward to deeper unravelling of these phenomena.
References
1. Maunsell JH, Treue S. (2006) Feature-based attention in visual cortex. Trends Neurosci. 29(6):317-22.
2. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. (2005) Invariant visual representation by single neurons in the human brain. Nature 23;435(7045):1102-7.
3. Li W, Piëch V, Gilbert CD. (2008) Learning to link visual contours. Neuron 7;57(3):442-51.
This viewpoint, observation as both active and reflexive, highlights a dichotomy present in debates concerning brain function in general. That is the distinction between seeing such neural exciation as occurring “bottom-up,” driven by the responses to incoming stimuli, versus “top-down,” controlled by higher level cognitive (dare I say conscious) processes.
For example, there is a Dalmatian (somewhat) hidden in figure 1. Without your knowledge of its presence, it is perhaps more natural to simply see a collection of dots, but once you've found that dog, its impossible to miss. This demonstrates that your perception of the object is not purely bottom-up. The image impinging on your photoreceptors alone doesn't necessarily lead to the experience of seeing the object. Other examples include seeing faces in clouds, a result of our overactive face recognition areas, or hearing words in the sounds produced by a gaggle of geese. These are both top-down examples, your existing perceptive mechanisms imposing themselves on the incoming information.
Charles Gilbert, a professor of Neurobiology at Rockefeller University, has been researching visual perception by recording from single neurons in the brains of monkeys for quite some time. A recent paper from his laboratory was aimed at quantifying the role of attention in the perception and cortical processing of a specific visual stimulus: long contours made of small individual line segments3. Figure 2 contains examples of these contours made from more (A) to fewer (C) subsegments .
There are single neurons in your visual cortex (area V2) that will become active when exposed to these larger, constructed contours in certain parts of the visual field. This response is built up from those of cells (in area V1) that are excited by exposure to small, continuous lines in particular places like the ones making up the larger edges above. The reactions of these cells are in turn shaped from the combination of many small, pixel-like bits of information, coming from the retina. This is the hierarchy of the visual system, the responses of neurons that represent progressively more complex objects are formed from earlier, simpler patterns, until we end up finally with neurons that respond best to images of your grandmother, or Bill Clinton, or Jennifer Aniston2.
This hierarchy is important to the theme of top-down versus bottom-up because it informs us as to what is at the top and what is at the bottom. It also allows us to construct a simple example that we can apply to more complicated cases.
The shapes in figure 3 are called Kanisza figures, but many scientists do refer to them as the pac-men that they bear more than a passing resemblance to. It is hard to ignore the triangle that seems to be formed by this particular configuration of polygons, despite the fact that each edge is missing a large segment. What’s happening here is that there are enough cells in V1 turned on, collinearly, by the partial edge of the invisible triangle to excite the V2 cell that would respond to a whole triangle edge in the same location. This is not the whole story however, it turns out that in addition to the bottom-up connections mediating the hierarchy I described before, there are also extensive feedback projections from higher areas like V2 to lower ones like V1. Thus, when the cell in V2 relays information up to higher areas that there appears to be a line spanning two of the Kanisza figures, it also informs all of the V1 cells that might be making up that line, including both the ones which are in this case actually being stimulated, and the interstitial ones where there is no edge to detect. The cells not receiving any actual visual stimulus are activated, to a lesser extent than they might be, by the feedback or top-down signal.
This then is the paradigm for top-down perception. Something like this is most likely happening in the Dalmatian example as well, with some high-level neuron that responds to dogs being activated and sending feedback signals down to all of the neurons that would normally activate the percept, facilitating the “segmentation” of the dog from the background.
What Gilbert and his colleagues did in order to more thoroughly understand this process was to engage a monkey in a task related to the perception of these incomplete contours while measuring the responses of the neurons in their area V1.
The monkey was presented with two images like the ones in figure 2 simultaneously, one in which some (1-9) of the line segments were oriented to form a contour, and one in which their angles were random. It’s task was to simply look at the one with the contour. Maybe because of something intrinsic to their visual system, or maybe just because the monkey didn't understand what the experimenters wanted them to do, they required extensive training before they became proficient at this game.
Before becoming experts at performing this chore, the cells in V1 which respond to the smaller constituent segments responded with a transient increase in their activity (figure 4, left). However, once they had become skilled at this task, the transient was followed by a prolonged bout of activity whose amplitude was proportional to the number of colinear segments making up the larger contour (figure 4, right). Intriguingly, even after the training, if the monkeys were anesthetized and exposed to the images the responses again showed only a transient increase, and no difference between the number of line segments making up the contour. What this suggests is that the engaged, top-down process of performing the task and attending to the stimuli is what generated the difference in the responses, thus active perception.
The idea of active perception brings to mind a question: what aspects of sensory experience are subject to this kind of cognitive control, and what are its limitations? Extreme cases are somewhat helpful: you can't see a circle when presented with a square, although every circle you've ever seen on a computer monitor or television is simply a lot of pixels, and thus not really a circle. This sort of fuzziness is certainly present in somatosensation (touch). An example, if I arrange a situation where your hand is hidden, but there is a rubber hand approximately where yours might be, I can (with a bit of "training") evoke a somatosensory experience in you by touching the rubber hand. Multiple pairings of poking your hidden hand while simultaneously having you watch me poke the rubber stand-in will lead to the feeling that any touch of the rubber hand is a touch of your hand. Further, I have certainly had the experience of enjoying the taste of something before knowing what it was, implying that the mere knowledge of the thing to be tasted can modify the experience of tasting it.
There must be some evolutionary/developmental aspect to all of this. At least in the vision example, if our evolution didn't provide us with feedback connections the likes of which I described above, there would be no anatomy to mediate the top-down control. Similarly, if our development didn't equip our visual systems with automatic detection systems for faces and lines and circles, there would be no high-level percept to feed-back down to lower systems, against which our brains might try and favorably compare incoming data.
The question then becomes, to what extent is this effect mediated by sheer brain circuitry and to what extent by the nebulous mystery that is conscious experience? I would have to argue that our brain circuitry is the only basis for our conscious experience, and thus any effect that we might attribute nonspecifically to our mental being represents our lack of knowledge about the connectivity in our skulls. However, my mother taught me that it is a great thing to be wrong, because that means you've got something to learn. So in any case, I look forward to deeper unravelling of these phenomena.
References
1. Maunsell JH, Treue S. (2006) Feature-based attention in visual cortex. Trends Neurosci. 29(6):317-22.
2. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. (2005) Invariant visual representation by single neurons in the human brain. Nature 23;435(7045):1102-7.
3. Li W, Piëch V, Gilbert CD. (2008) Learning to link visual contours. Neuron 7;57(3):442-51.
Monday, February 4, 2008
On Bodies and Brains
Being a dyed in the wool materialist, I believe that the cellular material hidden in our skulls creates our conscious experience. I was reminded this week, however, of just how irrevocably the body is involved in the generative process as well.
I read a piece of work which appears in the journal Cell, about a newly identified form of stimulated muscle contraction. Apparently, in the nematode Caenorhabditis Elegans, the muscles involved in expunging digested foosdtuffs from the body can be stimulated to do so directly by the intestinal tract1. Asim A. Beg et al, working in the lab of Erik M. Jorgensen, demonstrates that these muscles can be signaled that it's time to go to work by a high proton concentration, i.e. an acid. Thus, as the gut works, and the space between the intestine and the muscles becomes acidified, the muscles contract.
Normally, muscles are only commanded to produce a force by the release of neurotransmitter from neurons that specifically innervate these tissues. In other words, the nervous system must tell muscles when it's time to act. Of course the heart represents a notable counter-example, but there one finds specialized muscle cells that endogenously signal the heart to beat at regular intervals; native activity, not native stimulus response. The worm-gut case is unique because it is an example of the body bypassing the need for neural intervention.
I was further disabused of my cephalocentric ideology by listening to an old episode of RadioLab titled "Where am I?" That program contained several magnificent examples of the theme I'm expounding on, and one in particular that caught my attention.
The hosts of this not to be ignored radiological phenomenon had as a guest the science writer and scientist Robert M. Sapolsky (amongst others). He commented on a theory concerning brain-body interactions first attributed to William James. It goes like this: not only do the bodily physiological manifestations of emotional states precede conscious awareness of the source-stimulus, but those responses can themselves cause emotional experiences. I found this fascinating, and being a student of the brain, I wanted a little bit more information than the show had to offer, so I got in touch with Professor Sapolsky (at Stanford), and he gave me the following distillation (of the first part):
"The basic story is that sensory information (with the exception of olfaction) gets to the amygdala by way of the usual projections to the cortex, where there is classical sensory cortical processing, and with information eventually passed on to the amygdala. But there is an alternative pathway going straight from the thalamus to the amgdala, bypassing Cortex, so that information gets there sooner. So there’s the potential for amygdaloid activation in response to stimuli before there is conscious (i.e., cortical) awareness of the stimuli. So very fast, but because the cortex really does do all the important transformations of sensory information, this fast short-cut can be quite inaccurate."
This explains how -- by activating the amygdala -- emotions and concomitant corporal responses can occur before conscious awareness of their origins, but the bit about the body informing the emotional state goes further. The idea there is that even if you've decided on some rational level that there's nothing to be upset about, the body's state can convince the brain that there is.
I am not sure of the mechanism that the brain employs to read off the emotional state of the body, but this interplay between mental and body implies a couple of things. First, our body has the ability to inform our brains how we’re feeling, which is especially remarkable in light of the example of the body working independently of the brain, and second that how we’re feeling comes before what we think.
I suppose my dream of some day existing as a brain floating in a tank of reddish liquid is going to turn out far duller than I had imagined.
References
1. Beg AA, Ernstrom GG, Nix P, Davis MW, Jorgensen EM. (2008) Protons Act as a Transmitter for Muscle Contraction in C. elegans. Cell, 11;132(1):149-60.
I read a piece of work which appears in the journal Cell, about a newly identified form of stimulated muscle contraction. Apparently, in the nematode Caenorhabditis Elegans, the muscles involved in expunging digested foosdtuffs from the body can be stimulated to do so directly by the intestinal tract1. Asim A. Beg et al, working in the lab of Erik M. Jorgensen, demonstrates that these muscles can be signaled that it's time to go to work by a high proton concentration, i.e. an acid. Thus, as the gut works, and the space between the intestine and the muscles becomes acidified, the muscles contract.
Normally, muscles are only commanded to produce a force by the release of neurotransmitter from neurons that specifically innervate these tissues. In other words, the nervous system must tell muscles when it's time to act. Of course the heart represents a notable counter-example, but there one finds specialized muscle cells that endogenously signal the heart to beat at regular intervals; native activity, not native stimulus response. The worm-gut case is unique because it is an example of the body bypassing the need for neural intervention.
I was further disabused of my cephalocentric ideology by listening to an old episode of RadioLab titled "Where am I?" That program contained several magnificent examples of the theme I'm expounding on, and one in particular that caught my attention.
The hosts of this not to be ignored radiological phenomenon had as a guest the science writer and scientist Robert M. Sapolsky (amongst others). He commented on a theory concerning brain-body interactions first attributed to William James. It goes like this: not only do the bodily physiological manifestations of emotional states precede conscious awareness of the source-stimulus, but those responses can themselves cause emotional experiences. I found this fascinating, and being a student of the brain, I wanted a little bit more information than the show had to offer, so I got in touch with Professor Sapolsky (at Stanford), and he gave me the following distillation (of the first part):
"The basic story is that sensory information (with the exception of olfaction) gets to the amygdala by way of the usual projections to the cortex, where there is classical sensory cortical processing, and with information eventually passed on to the amygdala. But there is an alternative pathway going straight from the thalamus to the amgdala, bypassing Cortex, so that information gets there sooner. So there’s the potential for amygdaloid activation in response to stimuli before there is conscious (i.e., cortical) awareness of the stimuli. So very fast, but because the cortex really does do all the important transformations of sensory information, this fast short-cut can be quite inaccurate."
This explains how -- by activating the amygdala -- emotions and concomitant corporal responses can occur before conscious awareness of their origins, but the bit about the body informing the emotional state goes further. The idea there is that even if you've decided on some rational level that there's nothing to be upset about, the body's state can convince the brain that there is.
I am not sure of the mechanism that the brain employs to read off the emotional state of the body, but this interplay between mental and body implies a couple of things. First, our body has the ability to inform our brains how we’re feeling, which is especially remarkable in light of the example of the body working independently of the brain, and second that how we’re feeling comes before what we think.
I suppose my dream of some day existing as a brain floating in a tank of reddish liquid is going to turn out far duller than I had imagined.
References
1. Beg AA, Ernstrom GG, Nix P, Davis MW, Jorgensen EM. (2008) Protons Act as a Transmitter for Muscle Contraction in C. elegans. Cell, 11;132(1):149-60.
Wednesday, January 30, 2008
On Frequency Tuning
You are able to distinguish auditory frequencies smaller than the bandwidth (a measure of the range of frequencies to which a sensor will respond) of the cells in your inner ear that transduce sound from pressure waves into electrical impulses in your brain. As the authors of a recent paper appearing in Nature report, this is probably achieved through the use of population coding1. However, certain aspects of this phenomenon remain mysterious.
(Figure 1. Cartoon of what a sound sensor's response might look like, these numbers are not physically realistic)
Suppose we wanted to determine the bandwidth of a sensor having the response properties depicted above. A standard way to do so is the following: one measures the maximum response of the sensor (in this case, 10), and divides that value by two (thus, 5). Then one finds the smallest frequency which will produce that half-max response of 5 (a bit less than 8), and the largest frequency which produces that response (a bit greater than 12). The difference between the larger and smaller frequencies is termedthe bandwidth. Using this method, it's also called the full-width-at-half-max, for somewhat obvious reasons. We would then describe this sensor as having a central frequency of 10Hz, a Gaussian profile, and a bandwidth of 4Hz.
Now, let me reiterate: you are able to distinguish auditory frequencies smaller than the bandwidth (a measure of the range of frequencies to which a sensor will respond) of the cells that transduce sound from pressure waves into electrical impulses in your brain. This is odd for the following reason, suppose you were relying on the sensor above to tell you about what frequencies (pitches) of sound you were hearing. If I played you a sound at 8Hz and another at 12Hz, the response of the sensor (as you can see by the dotted lines on the figure above) would be identical. That sensor is unable to distinguish between sounds at 8Hz and sounds at 12Hz, yet somehow, your brain can. The way it achieves this feat is through population coding. What this means is that the brain almost always pools the responses of many sensory neurons in creating the conscious representations of sensory data that we experience.
A brief aside, you may be asking the question: Why don't we just have sensors with different response properties, linear, say? Like the figure below:
That would work nicely since the responses at 8Hz and 12Hz (and any other pair of frequencies for that matter) are distinct. However, it's very difficult to build biological sensors that have this kind of response profile, and in the interest of steering clear of unwieldy posts, I'll leave it at that.
Returning to population coding, I’ve said that the brain pools responses, but what does this mean exactly?
Let us now imagine that we examined the responses of two cells, with central frequencies of 9Hz and 11Hz, respectively. At 8Hz, cell 1’s response is ~8.5, and cell 2’s is ~2.5, while at 12Hz, the situation is flipped, with cell 1’s response being 2.5, and cell 2’s being 8.5. This reversal of fortunes is not intentional, not an inherent feature of this system, rather it is the result of my simplified illustration. These two cells are able to achieve in concert what a sole actor cannot: tell the difference between two sounds separated by a difference smaller than their individual bandwidths. All that is needed now is a further cell (in reality another layer of cells) to read off this code. “Whenever cell 1 says ‘8.5’ and cell 2 says ‘2.5,’ I know that sound is being played at 9Hz,” this further cell says.
This simplified view is not so far off from what we think is happening in the transformation of signals from the sensory periphery (your ear) to central processing areas (primary auditory cortex).
And now on to the mysterious facet mentioned earlier. These intrepid explorers of frequency tuning in primary auditory cortex found cells there with vary small bandwidths compared to sensory cells, implying that these cells were performing a computation similar to the one I’ve outlined above, but in order to test this hypothesis, they had to employ a different strategy than the one used for building frequency tuning curves.
In constructing the auditory response profile of a single cell, one generally uses single frequency sounds, pure tones. However, the brain was built to represent the real world, a place where single frequency sounds are essentially never encountered. Thus, the definition of a cell's response in this manner is necessarily lacking. Though it is possible, there is no reason to expect that one can predict the way a cell will respond to the simultaneous playback of 8Hz & 12Hz based on a simple summation of the individual responses elicited by 8Hz & 12Hz. Further, the heuristic version of population coding that I presented specifically does make that prediction, so recording the responses of these single cells to complex sounds allows these auditory neuroscientists to test their hypothesis concerning the underlying computation and the wiring of the brain.
Before I conclude, I want to mention that this research in particular is of a rare and important type, it is performed on humans. This is not some sort of needless invasion, it is unfortunately necessary to probe the electrical responses of the brains of epilepsy patients in order to remove certain small parts that cause their seizures.
It will probably come as no surprise that the responses predicted by the linear model I've discussed were quite distinct from those that the researchers found. This is exciting because it means that the brain has yet again provided a puzzle for us to solve. We know what the brain must be doing, but how, is the question presented. . Exploration of such quandaries can yield results that expand our general knowledge, be applied to other fields, and give us insight into the very nature of how we function. Such is the beauty of neuroscience.
References
1. Y. Bitterman, R. Mukamel, R. Malach, I. Fried & I. Nelken (2008) Ultra-fine frequency tuning revealed in single neurons of human auditory cortex. Nature 451, 197-201 | doi:10.1038/nature06476
Suppose we wanted to determine the bandwidth of a sensor having the response properties depicted above. A standard way to do so is the following: one measures the maximum response of the sensor (in this case, 10), and divides that value by two (thus, 5). Then one finds the smallest frequency which will produce that half-max response of 5 (a bit less than 8), and the largest frequency which produces that response (a bit greater than 12). The difference between the larger and smaller frequencies is termedthe bandwidth. Using this method, it's also called the full-width-at-half-max, for somewhat obvious reasons. We would then describe this sensor as having a central frequency of 10Hz, a Gaussian profile, and a bandwidth of 4Hz.
Now, let me reiterate: you are able to distinguish auditory frequencies smaller than the bandwidth (a measure of the range of frequencies to which a sensor will respond) of the cells that transduce sound from pressure waves into electrical impulses in your brain. This is odd for the following reason, suppose you were relying on the sensor above to tell you about what frequencies (pitches) of sound you were hearing. If I played you a sound at 8Hz and another at 12Hz, the response of the sensor (as you can see by the dotted lines on the figure above) would be identical. That sensor is unable to distinguish between sounds at 8Hz and sounds at 12Hz, yet somehow, your brain can. The way it achieves this feat is through population coding. What this means is that the brain almost always pools the responses of many sensory neurons in creating the conscious representations of sensory data that we experience.
A brief aside, you may be asking the question: Why don't we just have sensors with different response properties, linear, say? Like the figure below:
That would work nicely since the responses at 8Hz and 12Hz (and any other pair of frequencies for that matter) are distinct. However, it's very difficult to build biological sensors that have this kind of response profile, and in the interest of steering clear of unwieldy posts, I'll leave it at that.
Returning to population coding, I’ve said that the brain pools responses, but what does this mean exactly?
Let us now imagine that we examined the responses of two cells, with central frequencies of 9Hz and 11Hz, respectively. At 8Hz, cell 1’s response is ~8.5, and cell 2’s is ~2.5, while at 12Hz, the situation is flipped, with cell 1’s response being 2.5, and cell 2’s being 8.5. This reversal of fortunes is not intentional, not an inherent feature of this system, rather it is the result of my simplified illustration. These two cells are able to achieve in concert what a sole actor cannot: tell the difference between two sounds separated by a difference smaller than their individual bandwidths. All that is needed now is a further cell (in reality another layer of cells) to read off this code. “Whenever cell 1 says ‘8.5’ and cell 2 says ‘2.5,’ I know that sound is being played at 9Hz,” this further cell says.
This simplified view is not so far off from what we think is happening in the transformation of signals from the sensory periphery (your ear) to central processing areas (primary auditory cortex).
And now on to the mysterious facet mentioned earlier. These intrepid explorers of frequency tuning in primary auditory cortex found cells there with vary small bandwidths compared to sensory cells, implying that these cells were performing a computation similar to the one I’ve outlined above, but in order to test this hypothesis, they had to employ a different strategy than the one used for building frequency tuning curves.
In constructing the auditory response profile of a single cell, one generally uses single frequency sounds, pure tones. However, the brain was built to represent the real world, a place where single frequency sounds are essentially never encountered. Thus, the definition of a cell's response in this manner is necessarily lacking. Though it is possible, there is no reason to expect that one can predict the way a cell will respond to the simultaneous playback of 8Hz & 12Hz based on a simple summation of the individual responses elicited by 8Hz & 12Hz. Further, the heuristic version of population coding that I presented specifically does make that prediction, so recording the responses of these single cells to complex sounds allows these auditory neuroscientists to test their hypothesis concerning the underlying computation and the wiring of the brain.
Before I conclude, I want to mention that this research in particular is of a rare and important type, it is performed on humans. This is not some sort of needless invasion, it is unfortunately necessary to probe the electrical responses of the brains of epilepsy patients in order to remove certain small parts that cause their seizures.
It will probably come as no surprise that the responses predicted by the linear model I've discussed were quite distinct from those that the researchers found. This is exciting because it means that the brain has yet again provided a puzzle for us to solve. We know what the brain must be doing, but how, is the question presented. . Exploration of such quandaries can yield results that expand our general knowledge, be applied to other fields, and give us insight into the very nature of how we function. Such is the beauty of neuroscience.
References
1. Y. Bitterman, R. Mukamel, R. Malach, I. Fried & I. Nelken (2008) Ultra-fine frequency tuning revealed in single neurons of human auditory cortex. Nature 451, 197-201 | doi:10.1038/nature06476
Monday, January 28, 2008
On Sex
Sex changes things. The act of intercourse between two people inevitably changes some aspect of a pair’s interaction. This may be a dual effect, or it may simply manifest from modifications of the behavior of the individuals.
As human beings, we tend to focus more on the psychological or sociological implications of coupling, but there are also biological ramifications that can be independent of or intertwined with cognitive facets of sexual congress. It is thought that many of the behavioral effects have evolved to increase the likelihood that a union yields offspring, or simply to increase the reproductive success of one of the putative parents.
A recent article appearing in Nature highlights one striking example of such a convolved effect, the so-called sex peptide and its cognate receptor in the fruit fly Drisophila Melanogaster. The work, from the lab of Barry J. Dickson at the Research Institute of Molecular Pathology in Vienna, both identifies the receptor and the sites at which it can be found in the fly’s body1.
It had been known for some time that sex peptide (found in the male’s seminal fluid), acts on the female in such a way that she is more likely to lay eggs, and less likely to copulate again. This has the effect of increasing the male’s chance of producing heirs; keeping his sperm from being diluted by competing sires, and increases the number of potential progeny. Of course, this has a cost for the female, as she is now less likely to find a more-fit partner.
There are also many other examples worth mentioning (these were brought to my attention by an excellent comment on the paper, also in Nature2):
In mammals, intercourse alters the environment of the reproductive tract at an immunological level, increasing the probability of fertilization and implantation, perhaps explaining the increased likelihood that a female can contract a urinary tract infection.
The males of many species (avian, reptile, rodent) will simply guard a female post-coitally to prevent her from finding a (potentially) more desirable mate.
In snakes and some insects, sex can actually lead to changes in the pheromones that a female produces, rendering her less attractive to other possible suitors.
If a female rodent is exposed to the pheromones of a dominant male, she develops a sort of taste for power, making her more likely to reject a subordinate gentleman caller.
Female jewel wasps have an even simpler version: their response to male sex-pheromones changes from attraction to aversion after copulation. Similarly, and again in the fruit fly, females will actively turn-down new beaus, running away from newly encountered males and even kicking them.
While there is no known human homologue of the genes identified by the study I mentioned above, all of these examples beg the question of what forms of this sex-mind-control might be at work in our species. It’s also interesting to consider that all the thus-far identified effects are of males on females, could there also be some method that women have of controlling the men as well? In any case, knowing that our very biology might change as the result of certainly make me wonder what might at work in my brain & body after a roll in the hay.
References
1. Yapici, N. et al (2008) A receptor that mediates the post-mating switch in Drosophila reproductive behaviour. Nature 451, 33–37
2. Griffith L.C. (2008) Neuroscience: Love hangover. Nature 451, 24-25
Thursday, December 13, 2007
On Making Faces
How do babies learn to make faces? With arm or leg movements, it seems plausible that, as William James suggested, one might gain insight from simply associating observed appendage position with concurrent muscle activation patterns1. However, in contrast to an attempted stirring of limbs, the infant cannot see what results from the activation of his facial muscles. Thus, the learning mechanism cannot rely on sensory confirmation that the indented action was successfull. That new humans very rapidly learn to express their emotional state through a smile, frown or furrowing of brow hints that there is some implicit path to the acquisition of this skill. It has been suggested by some that the Mirror Neuron (MN) system constitutes this road, or at least a map of it2.
In the adult, MNs are cells that respond when an animal performs some act – picking up a piece of fruit, say - and when it observes another individual doing the same thing, even if the observed actor is a member of another species or stranger, a robot. Beyond this, their activation potentiates the pathways that would be involved in the execution of such a movement by the observer: producing measurable sub-threshold responses in the muscles involved. The suggestion that this system is involved in motor learning and imitation from birth amounts to the assumption that MNs are prenatally wired to function as they do in the mature brain. This is a very attractive idea as it removes the need for some external form of reinforcement - like a visual confirmation of the completed movement - to inform the motor-learning process. Instead, the responsibility for being both carrot and stick is shifted internally, to the MNs. The proposition is that the genetically defined circuitry imbues the MNs with a "knowledge" of the pattern of muscle activity associated with both an observed or executed behavior.
This, unsurprisingly, presents a further question: if the MN system is present from birth and possesses such information as described above, why do infants need to learn how to move at all? This is where we must tread a bit into the realm of informed speculation. First, the MN system cannot know how to execute every movement possible: for instance it certainly cannot know at birth the set of motor commands associated with performing some complex gymnastic move, say a double backflip. If the MN is an artifact of evolution, then it is likely that there is a continuum of innate interpretability, from simple acts like smiles that are well known to the MN system to more rare or contemporary behaviors like figure skating or fixing a bicycle. Thus, using the MN system as a template, of sorts, can only be effective for behaviors on the oft-encountered end of the spectrum. Second, since babies sadly do not leap from the the womb as masters of muscular control, the wiring of the MN system must itself develop at a pace commensurate with the time-course of an individual's behavioral procurement.
How is it then, that this internal electro-cultivation proceeds in lock-step with the infant's newfound agility? It has been well documented that humans lose half the total number of neurons in their central nervous system by the time they've reached six months of age. This pruning is a synaptic refinement process, also termed neural darwinism4. It is quite beneficial to the newborn animal, since his neural connectivity is far too manifest, too spatially noisy, and must be cleaned up. This happens in all areas of cerebral cortex. For example, in the visual system, molecular cues guide the axons of nearby retinal ganglion cells to adjacent targets in the thalamus while the animal is still in the womb in a gross way, but it is the activity arising from visual stimulation which pares down the connections to the state we see them in the adult. the exquisite spatial precision of connections between the retina and thalamus cells in the retina are connected to cells in the thalamus with exquisite spatial precision because of visual experience. It was Hebb who pointed out that cells that "fire together wire together.5" This means that if two retinal cells fire at the same time, they will tend to be connected to the same post-synaptic target. That is not to say that any two cells that fire at the same time anywhere in the brain will inevitably be connected to each-other, but rather that in deciding which of the molecularly defined crude connections to keep, a post synaptic cell will retain those which tend to fire at the same time in response to stimulation. The stimuli that cause the retinal cells to fire are not simply sets of independently fluctuating pixels; rather they are full of spatial correlations. If a single vertical line passed across your field of vision, a single line of cells in the retina would be stimulated at once as the line moved by. It is almost never the case that two abutting photoreceptors see completely uncorrelated (in time) patterns of illumination. In this way, the spatial relationships in the image translate into spatial relationships in the connections in the brain.
The goings-on I've outlined above might generally be termed activity dependent synaptic refinement (ADSR). What I'm hypothesizing is that, as with vision, some form of ADSR is at work in those cortical-areas involved in learning how to move: that the very act of generating motor output leads to more stereotyped action through application of the Hebbian "fire together wire together" motif. The commonality between vision and movement, and indeed the unifying principle behind ADSR, is that the systems are exploiting the presence of underlying statistical content. While the MN system is biasing motor output towards certain configurations, the motor cortex is learning about the possible relationships between muscle tensions, lengths and contractile velocities. It is thought that the brain might use such a mechanism universally as an attempt at maximizing efficiency. For example: if you always listen to symphonic classical music, you might set your stereo to boost bass & treble, but if you're more the piano concerto type, you'd want a touch more mid-range; then again, if your tastes are more varied, it might make sense to emphasize all three frequency bands equally so as not to aurally marginalize any particular genre. Another for instance more relevant to motor output: when you drive in the city, you rarely ever get out of 2nd gear, but on the highway, you're almost always in the upper gears. In the same sense, the brain attempts to use the Hebbian rule to optimize its (sensory) inputs and (motor) outputs to the sensory stimuli impinging on it and the motor programs it generates.
You may have noticed in all this that I've skipped over one important point: how (genetic wiring notwithstanding), do mirror neurons extract the information about a movement being performed by another agent? It is not at all understood how this occurs in the adult, so how it could be happening in babies is even more mysterious, especially in light of the messiness of infant brains that I've spoken of. It simply must be the case that visual stimuli are translated into data concerning the movement of bodies. It is possible that specialized structures for recognizing arms and hands, faces and feet, become sophisticated very early on, but nothing like this has been observed to-date. The needed course of research is clear, but developing experiments to elucidate what might be at work is not. We can only wait and watch from the wings while the scientific players act, and perhaps deliver soto voce direction from time to time.
References
1. James, W. The Principles of Psychology Vol. 1. Henry Holt & Company (1890)
2. Lepage JF, Théoret H. (2007) The mirror neuron system: grasping others' actions from birth? Dev Sci. 10(5):513-23.
3. Rizzolatti, G., & Craighero, L., (2004) The Mirror Neuron System. Annu. Rev. Neurosci. 27, 169-192.
4. Hebb, D.O. The Organization of Behavior. John H. Wiley & Sons (1967)
5. Edelman, G.M. Neural Darwinism. Oxford Paperbacks (1990)
In the adult, MNs are cells that respond when an animal performs some act – picking up a piece of fruit, say - and when it observes another individual doing the same thing, even if the observed actor is a member of another species or stranger, a robot. Beyond this, their activation potentiates the pathways that would be involved in the execution of such a movement by the observer: producing measurable sub-threshold responses in the muscles involved. The suggestion that this system is involved in motor learning and imitation from birth amounts to the assumption that MNs are prenatally wired to function as they do in the mature brain. This is a very attractive idea as it removes the need for some external form of reinforcement - like a visual confirmation of the completed movement - to inform the motor-learning process. Instead, the responsibility for being both carrot and stick is shifted internally, to the MNs. The proposition is that the genetically defined circuitry imbues the MNs with a "knowledge" of the pattern of muscle activity associated with both an observed or executed behavior.
This, unsurprisingly, presents a further question: if the MN system is present from birth and possesses such information as described above, why do infants need to learn how to move at all? This is where we must tread a bit into the realm of informed speculation. First, the MN system cannot know how to execute every movement possible: for instance it certainly cannot know at birth the set of motor commands associated with performing some complex gymnastic move, say a double backflip. If the MN is an artifact of evolution, then it is likely that there is a continuum of innate interpretability, from simple acts like smiles that are well known to the MN system to more rare or contemporary behaviors like figure skating or fixing a bicycle. Thus, using the MN system as a template, of sorts, can only be effective for behaviors on the oft-encountered end of the spectrum. Second, since babies sadly do not leap from the the womb as masters of muscular control, the wiring of the MN system must itself develop at a pace commensurate with the time-course of an individual's behavioral procurement.
How is it then, that this internal electro-cultivation proceeds in lock-step with the infant's newfound agility? It has been well documented that humans lose half the total number of neurons in their central nervous system by the time they've reached six months of age. This pruning is a synaptic refinement process, also termed neural darwinism4. It is quite beneficial to the newborn animal, since his neural connectivity is far too manifest, too spatially noisy, and must be cleaned up. This happens in all areas of cerebral cortex. For example, in the visual system, molecular cues guide the axons of nearby retinal ganglion cells to adjacent targets in the thalamus while the animal is still in the womb in a gross way, but it is the activity arising from visual stimulation which pares down the connections to the state we see them in the adult. the exquisite spatial precision of connections between the retina and thalamus cells in the retina are connected to cells in the thalamus with exquisite spatial precision because of visual experience. It was Hebb who pointed out that cells that "fire together wire together.5" This means that if two retinal cells fire at the same time, they will tend to be connected to the same post-synaptic target. That is not to say that any two cells that fire at the same time anywhere in the brain will inevitably be connected to each-other, but rather that in deciding which of the molecularly defined crude connections to keep, a post synaptic cell will retain those which tend to fire at the same time in response to stimulation. The stimuli that cause the retinal cells to fire are not simply sets of independently fluctuating pixels; rather they are full of spatial correlations. If a single vertical line passed across your field of vision, a single line of cells in the retina would be stimulated at once as the line moved by. It is almost never the case that two abutting photoreceptors see completely uncorrelated (in time) patterns of illumination. In this way, the spatial relationships in the image translate into spatial relationships in the connections in the brain.
The goings-on I've outlined above might generally be termed activity dependent synaptic refinement (ADSR). What I'm hypothesizing is that, as with vision, some form of ADSR is at work in those cortical-areas involved in learning how to move: that the very act of generating motor output leads to more stereotyped action through application of the Hebbian "fire together wire together" motif. The commonality between vision and movement, and indeed the unifying principle behind ADSR, is that the systems are exploiting the presence of underlying statistical content. While the MN system is biasing motor output towards certain configurations, the motor cortex is learning about the possible relationships between muscle tensions, lengths and contractile velocities. It is thought that the brain might use such a mechanism universally as an attempt at maximizing efficiency. For example: if you always listen to symphonic classical music, you might set your stereo to boost bass & treble, but if you're more the piano concerto type, you'd want a touch more mid-range; then again, if your tastes are more varied, it might make sense to emphasize all three frequency bands equally so as not to aurally marginalize any particular genre. Another for instance more relevant to motor output: when you drive in the city, you rarely ever get out of 2nd gear, but on the highway, you're almost always in the upper gears. In the same sense, the brain attempts to use the Hebbian rule to optimize its (sensory) inputs and (motor) outputs to the sensory stimuli impinging on it and the motor programs it generates.
You may have noticed in all this that I've skipped over one important point: how (genetic wiring notwithstanding), do mirror neurons extract the information about a movement being performed by another agent? It is not at all understood how this occurs in the adult, so how it could be happening in babies is even more mysterious, especially in light of the messiness of infant brains that I've spoken of. It simply must be the case that visual stimuli are translated into data concerning the movement of bodies. It is possible that specialized structures for recognizing arms and hands, faces and feet, become sophisticated very early on, but nothing like this has been observed to-date. The needed course of research is clear, but developing experiments to elucidate what might be at work is not. We can only wait and watch from the wings while the scientific players act, and perhaps deliver soto voce direction from time to time.
References
1. James, W. The Principles of Psychology Vol. 1. Henry Holt & Company (1890)
2. Lepage JF, Théoret H. (2007) The mirror neuron system: grasping others' actions from birth? Dev Sci. 10(5):513-23.
3. Rizzolatti, G., & Craighero, L., (2004) The Mirror Neuron System. Annu. Rev. Neurosci. 27, 169-192.
4. Hebb, D.O. The Organization of Behavior. John H. Wiley & Sons (1967)
5. Edelman, G.M. Neural Darwinism. Oxford Paperbacks (1990)
Subscribe to:
Posts (Atom)