"But no matter. I had my little revenge in due time. A man from Pasadena told me one day that Mrs. Maximovich née Zborovski had died in childbirth around 1945; the couple had somehow got over to California and had been used there, for an excellent salary, in a year-long experiment conducted by a distinguished American ethnologist. The experiment dealt with human and racial reactions to a diet of bananas and dates in a constant position on all fours. My informant, a doctor, swore he had seen with his own eyes obese Valechka and her colonel, by then gray-haired and also quite corpulent, diligently crawling about the well-swept floors of a brightly lit set of rooms (fruit in one, water in another, mats in a third and so on) in the company of several other hired quadrupeds, selected from indigent and helpless groups. I tried to find the results of these tests in the Review of Anthropology; but they appear not to have been published yet. These scientific products take of course some time to fructuate. I hope they will be illustrated with photographs when they do get printed, although it is not very likely that a prison library will harbor such erudite works."-Lolita, Chapter 6
Saturday, July 30, 2011
I Don't Think the Ethics Committee Would Approve That
Monday, July 25, 2011
Walk Along the Paper Trail: Satiating Trail Mix
In the last trek we took, I wrote about two seminal papers that describe taste coding in gustatory cortex. Today I'm going to cover a followup paper which described satiety's effects on gustatory coding.
Let me fill you up
The neural systems involved in satiety, food intake, and body weight are incredibly intricate. First, there are endocrine responses: after food intake, leptin and insulin are released, blood sugar increases, and cannabinoid levels drop. Then, as you get hungry again, leptin, insulin, and blood glucose levels drop while endocannabinoid increases.
These endocrine molecules bind to a variety of receptors on the tongue, and throughout the brain, including the hypothalamus. On the tongue, leptin receptors are expressed on sweet taste bud cells, and leptin binding causes a decrease in firing, presumably decreasing perceived sweetness. In contrast, cannabinoid receptors on taste bud cells can sensitize the taste response, increasing perceived sweetness.
The other well described pathway for endocrine signaling is via the hypothalamus. I'll probably delve into this later, but in brief, the hypothalamus has multiple cell populations which express endocrine receptors, and can directly regulate feeding behaviour. For example, Aponte and Sternson recently showed that if you stimulate AGRP-expressing neurons in the hypothalamus, you can induce feeding behaviour; while stimulation of POMC-expressing neurons over 24h reduces food intake.
Given all these ways that satiety can be regulated, what happens in gustatory cortex?
CNS coding of taste
To see how satiety influences the CNS, you first need to establish a metric for satiety. de Araujo and colleagues chronically implanted rats with electrodes, and then gave them limited access to a sucrose solution. The sucrose was behind a gate that would only stay open for five seconds after a lick. Two seconds after closing, the gate would reopen, allowing the rat to lick again. They then measured the inter-trial-interval (ITI) to quantify how motivated the rats were to get some sugar (panel A).
In 16/19 animals they found distinct hunger and satiety phases, with ITIs of 18s during hunger1, 180s during satiety, and 35s during "hunger2." On Saturday, I wrote a mini-review on mouse licking, and showed that mice lick in bouts with a regular frequency. Here they also analyzed the microstructure of the licking bouts during hunger and satiety, and found the rats licked at ~6.5 Hz in both phases.
Once they categorized well-defined hunger and satiety phases, they then characterized the neural response in four brain areas: orbitofrontal cortex (OFC), gustatory cortex (INS; insular cortex), the lateral hypothalamus (LH), and the amygdala (AM). As you may remember from last time, 30-40% of neurons in gustatory cortex encode taste information; I'm not well read on taste responses in other areas, but I think the percentage is similar. Since they are applying a single tastant, sucrose, one might expect the percentage to be lower here.
Of the 625 neurons recorded in all the areas, they found 101 (16%) were licking related, while 152 (24%) responded to taste delivery. 179 (29%) neurons had firing changes related to satiety, but none of these were licking-related cells (panel F). Of these, most (104/179) responded simply by changing their baseline firing rate (panels D & E). For the other 75, their responses to sucrose were altered, with both increases and decreases in firing (panel A-C). Satiety-related responses were found in all four brain areas measured, but some had more responsive neurons than others. (LH > AM > INS > OFC).
So far I have described the single cell response, but what about the population response? They looked at this by calculating the mean population firing rate, and found that for 12/19 sessions, mean firing rate decreased during satiety (panel A, below). However, this seems fairly dirty, given that they were able to calculate individual cells' changes in firing rate. They continued this analysis for the next two figures, looking at the population's performance vs individual cells, but to be honest, I don't find it compelling.
I have a few issues with this paper. First, the temporal analysis is rather shallow: when they characterized cells responsiveness, they simply binned the 500ms before and after a lick. However, five years earlier, they showed that taste responses could have complex temporal profiles, including periods of inhibition and excitation in the same response. Second, I wish they had used more than one tastant, given that there are leptin receptors on sucrose taste bud cells. Using other tastants would help differentiate between endocrine effects on the tongue versus effects via the hypothalamus or other areas. Finally, I would have liked to have seen a more sophisticated population analysis, even just looking at cross-correlation within ensembles.
Given those caveats, there are a lot of interesting results. First, there is the most common modulation, changes in baseline firing rate. This could influence coding in a few different ways, changing the gain or dynamic range of firing, or changing the potential for synchrony between neurons. That there are more modulations of baseline firing than taste responses seems significant.
Second, the changes in firing rates of these neurons are quite long (see panels C&D in the last figure), and can continue beyond the end of satiated behaviour. Satiety is a long-term process, changing gradually over hours, so it make sense that neurons' behaviour would also be long term. Perhaps mice's perception of hunger and satiety are faster given their metabolism.
Finally, it was interesting to see there were satiety changes in all areas (it would have been nice to see a table with a complete breakdown). The changes in hypothalamus make sense given its involvement in feeding, and orbitofrontal cortex makes sense given its executive function. But there were also changes in the amygdala and gustatory cortex. This could be due to common processes influencing all areas (e.g. reduction in taste receptor sensitivity), or could simply be indicative of how complicated body weight and food intake regulation is.
de Araujo IE, Gutierrez R, Oliveira-Maia AJ, Pereira A Jr, Nicolelis MA, & Simon SA (2006). Neural ensemble coding of satiety states. Neuron, 51 (4), 483-94 PMID: 16908413
Let me fill you up
The neural systems involved in satiety, food intake, and body weight are incredibly intricate. First, there are endocrine responses: after food intake, leptin and insulin are released, blood sugar increases, and cannabinoid levels drop. Then, as you get hungry again, leptin, insulin, and blood glucose levels drop while endocannabinoid increases.
These endocrine molecules bind to a variety of receptors on the tongue, and throughout the brain, including the hypothalamus. On the tongue, leptin receptors are expressed on sweet taste bud cells, and leptin binding causes a decrease in firing, presumably decreasing perceived sweetness. In contrast, cannabinoid receptors on taste bud cells can sensitize the taste response, increasing perceived sweetness.
The other well described pathway for endocrine signaling is via the hypothalamus. I'll probably delve into this later, but in brief, the hypothalamus has multiple cell populations which express endocrine receptors, and can directly regulate feeding behaviour. For example, Aponte and Sternson recently showed that if you stimulate AGRP-expressing neurons in the hypothalamus, you can induce feeding behaviour; while stimulation of POMC-expressing neurons over 24h reduces food intake.
Given all these ways that satiety can be regulated, what happens in gustatory cortex?
CNS coding of taste
To see how satiety influences the CNS, you first need to establish a metric for satiety. de Araujo and colleagues chronically implanted rats with electrodes, and then gave them limited access to a sucrose solution. The sucrose was behind a gate that would only stay open for five seconds after a lick. Two seconds after closing, the gate would reopen, allowing the rat to lick again. They then measured the inter-trial-interval (ITI) to quantify how motivated the rats were to get some sugar (panel A).
 |
| ITI reflects mouse satiety. A. Schematic of licking paradigm, and ITI definition. B. Example ITI for a rat going through hunger-satiety-hunger cycle. C. Population ITI. D. Measured blood glucose in these three phases. E. Measured insulin in the three phases. From de Araujo et al, 2006. |
Once they categorized well-defined hunger and satiety phases, they then characterized the neural response in four brain areas: orbitofrontal cortex (OFC), gustatory cortex (INS; insular cortex), the lateral hypothalamus (LH), and the amygdala (AM). As you may remember from last time, 30-40% of neurons in gustatory cortex encode taste information; I'm not well read on taste responses in other areas, but I think the percentage is similar. Since they are applying a single tastant, sucrose, one might expect the percentage to be lower here.
Of the 625 neurons recorded in all the areas, they found 101 (16%) were licking related, while 152 (24%) responded to taste delivery. 179 (29%) neurons had firing changes related to satiety, but none of these were licking-related cells (panel F). Of these, most (104/179) responded simply by changing their baseline firing rate (panels D & E). For the other 75, their responses to sucrose were altered, with both increases and decreases in firing (panel A-C). Satiety-related responses were found in all four brain areas measured, but some had more responsive neurons than others. (LH > AM > INS > OFC).
 |
| Examples of cells modulated by satiety. Event rasters are shown above peri-event time histograms. Satiety border shown as dashed blue line. A. This cell lost its sucrose response during satiety. B. This cell lost its anticipatory response. C. This cell gained an anticipatory response. D&E. Changes in baseline firing rate. F. Licking related neurons do not change firing. From de Araujo et al, 2006. |
 |
| Population firing during satiety. A. The population firing rate decreases during satiety. B. ITI showing satiety. C-E. Example cells from the population. Only cell E is bimodal. From de Araujo et al, 2006. |
Given those caveats, there are a lot of interesting results. First, there is the most common modulation, changes in baseline firing rate. This could influence coding in a few different ways, changing the gain or dynamic range of firing, or changing the potential for synchrony between neurons. That there are more modulations of baseline firing than taste responses seems significant.
Second, the changes in firing rates of these neurons are quite long (see panels C&D in the last figure), and can continue beyond the end of satiated behaviour. Satiety is a long-term process, changing gradually over hours, so it make sense that neurons' behaviour would also be long term. Perhaps mice's perception of hunger and satiety are faster given their metabolism.
Finally, it was interesting to see there were satiety changes in all areas (it would have been nice to see a table with a complete breakdown). The changes in hypothalamus make sense given its involvement in feeding, and orbitofrontal cortex makes sense given its executive function. But there were also changes in the amygdala and gustatory cortex. This could be due to common processes influencing all areas (e.g. reduction in taste receptor sensitivity), or could simply be indicative of how complicated body weight and food intake regulation is.
de Araujo IE, Gutierrez R, Oliveira-Maia AJ, Pereira A Jr, Nicolelis MA, & Simon SA (2006). Neural ensemble coding of satiety states. Neuron, 51 (4), 483-94 PMID: 16908413
Saturday, July 23, 2011
Paper Trail Day Trip: Mouse Lick Throughs
Last week at lab meeting, I found myself having the absurd discussion of how to best train a mouse to lick a water spout. Mice don't have many ways to talk to us dumb humans, and head-fixed mice have even less. The best way we have is licking.
After lab meeting, I did some Google Scholaring, and found a great little article that quantified mouse lick rates. In two mouse strains! Comparative mouse licking ethology! It doesn't merit a full blog post, but I wanted to highlight the main findings for all you other mouse lickers out there.
They used a lickometer (pardon the technical jargon) to measure how often C57 and DBA/2J mice licked. They found mice licked in bouts of 1-20s, about once a minute.
As you can see in the inter-lick interval (ILI) histogram above (panel c), licking is highly regular, but mice occasionally miss a lick, either due to lickometer error, or actual pauses in licking. There is a much smaller third harmonic. During the bouts, C57 mice lick at 8Hz, while DBA mice lick at 10Hz.
Since C57 mice lick at a slower rate than DBA mice, they compensate by having more licking bursts (compare black and white bars). The average lick volume for both strains was 1.2uL.
After lab meeting, I did some Google Scholaring, and found a great little article that quantified mouse lick rates. In two mouse strains! Comparative mouse licking ethology! It doesn't merit a full blog post, but I wanted to highlight the main findings for all you other mouse lickers out there.
They used a lickometer (pardon the technical jargon) to measure how often C57 and DBA/2J mice licked. They found mice licked in bouts of 1-20s, about once a minute.
 |
| Mouse licking. A. Mice lick in bursts. B. Individual bursts have regular licking. C. Inter-lick-interval during bursts. From Boughter et al 2010. |
 |
| C57/B6 mice have more licking bouts than DBA/2J mice, but lick more slowly during them. From Boughter et. al. 2010. |
Since C57 mice lick at a slower rate than DBA mice, they compensate by having more licking bursts (compare black and white bars). The average lick volume for both strains was 1.2uL.
I love that papers like this exist, because their kinda handy, and kinda absurd. Another paper along these lines is, "Distribution of serotonin immunoreactivity int he main olfactory bulb of the Mongolian gerbil." Just don't tell Sarah Palin.
Boughter Jr, J., Baird, J. P., Bryant, J., St John, S., and Heck, D. (2007). C57BL/6J and DBA/2J mice vary in lick rate and ingestive microstructure. Genes, Brain and Behavior 6, 619–627.
Thursday, July 21, 2011
Spines Only Grow Once
Continuing my series of publishing extra data from my graduate work (probably only 1-2 more posts left), today I'm going to talk about my favourite experiment I ever did.
Spine size is a proxy for memory
The standard cellular model for learning and memory is that memories are stored in the synapses between neurons, and that learning changes the strength of these synapses. While this model makes sense, no one has actually been able to measure the strength of a synapse while an animal learns. However, you can use spine size as a proxy for synaptic strength: among other things, bigger spines have more AMPA receptors and stronger synaptic currents. If you image spines while animals are learning you can see all sorts of changes in spine number, and watch spines be created and destroyed.
Since spine size is correlated with synaptic strength, and synaptic strengths change, Haruo Kasai theorized that a spine's size actually represents the stimulation history of a synapse. That is, a small spine represents a synapse that is either newly formed, or has been depotentiated; and a large spine represents a synapse that has been repeatedly potentiated. There are lots of cool questions you can ask if this is true, like whether there are a discrete number of spine sizes, or if spine size is graded; and whether there is a continuous, random process of spine shrinkage that allows us to form new memories.
Double uncaging, OMG
If a spine's size can represent its stimulus history, this implies that a spine/synapse can repeatedly change size. Some people have tried to test this using a minimal stimulation technique, but because they could not identify the synapse they were recording, the results are not 100% conclusive. Another group used glutamate uncaging and found that synaptic strength changed in a step-wise fashion, but did not stimulate twice.
Two-photon glutamate uncaging allows you to address this question. You can measure a spine's size (i.e. synaptic strength), and stimulate it with glutamate to cause an increase in spine size.
This allows one to answer the question: what happens if you stimulate the same spine twice? I started by stimulating the same spine twice, at intervals of 15 min. (standard Yasuda lab uncaging protocol: 4mM MNI glutamate, 6mW power, 6ms pulses, 30x stim, 0.5 Hz). Following the first stimulation, the spine grew to +310% of its original volume, and decayed to +100% fifteen minutes following stimulation (panel A). I then repeated the same stimulation protocol. After the second stimulation, the peak spine volume was +360%, with a volume at 15 min. of +135%. (Figure saved at FigShare.)
There is a lot of variability in the transient phase of structural plasticity (<5 min), so it's best to look at structural plasticity after 30 min. To do this, I uncaged on nearby "control" spines (panel B). If you compare the structural plasticity for the two spines at 30 min., you can see they're basically the same.
So that's at 15 minutes, what about longer time intervals? I repeated the experiment with a 60 minute interval, and saw the same basic result: following the second stimulation, there was no obvious increase in structural plasticity, for both the transient and sustained phases (panel C).
So how do I interpret these results? First, I think this shows that structural plasticity has a refractory period; that is, once a synapse changes strength, it is stable, and cannot be changed again for a while. How long this refractory period lasts is a great question, and could be a limiting factor in memory formation. I tried stimulating twice with an interval of 24 hours, but the slices got contaminated by bacteria.
A second interpretation is that structural plasticity is saturable. That is, the capacity for change has a limit at any given time point. Note that this does not really address the question of whether spine sizes are distributed over a continuous space, or discrete sizes.
What I really love about this experiment is how simple it is, and how many different directions you can go from here. And it was all enabled by new technology.
Spine size is a proxy for memory
The standard cellular model for learning and memory is that memories are stored in the synapses between neurons, and that learning changes the strength of these synapses. While this model makes sense, no one has actually been able to measure the strength of a synapse while an animal learns. However, you can use spine size as a proxy for synaptic strength: among other things, bigger spines have more AMPA receptors and stronger synaptic currents. If you image spines while animals are learning you can see all sorts of changes in spine number, and watch spines be created and destroyed.
Since spine size is correlated with synaptic strength, and synaptic strengths change, Haruo Kasai theorized that a spine's size actually represents the stimulation history of a synapse. That is, a small spine represents a synapse that is either newly formed, or has been depotentiated; and a large spine represents a synapse that has been repeatedly potentiated. There are lots of cool questions you can ask if this is true, like whether there are a discrete number of spine sizes, or if spine size is graded; and whether there is a continuous, random process of spine shrinkage that allows us to form new memories.
Double uncaging, OMG
If a spine's size can represent its stimulus history, this implies that a spine/synapse can repeatedly change size. Some people have tried to test this using a minimal stimulation technique, but because they could not identify the synapse they were recording, the results are not 100% conclusive. Another group used glutamate uncaging and found that synaptic strength changed in a step-wise fashion, but did not stimulate twice.
Two-photon glutamate uncaging allows you to address this question. You can measure a spine's size (i.e. synaptic strength), and stimulate it with glutamate to cause an increase in spine size.
 |
| Stimulating a spine increases spine volume. Neurons are transfected with fluorophores so you can see them. Before stimulation (top row), spine are relatively small; one minute after stimulation, they are much larger; 30 minutes later, their size has relaxed, but is still larger than originally. From Patterson et. al. 2010. |
 |
| Stimulating the same spine twice does not cause growth beyond the first stimulation. A. Double stimulation at 15 min. interval. n=11 spines, 10 cells. B. Paired spine structural plasticity. n=5 pairs. C. Double stimulation at 60 min. interval. n=8 spines, 7 cells. Unpublished data! |
So that's at 15 minutes, what about longer time intervals? I repeated the experiment with a 60 minute interval, and saw the same basic result: following the second stimulation, there was no obvious increase in structural plasticity, for both the transient and sustained phases (panel C).
So how do I interpret these results? First, I think this shows that structural plasticity has a refractory period; that is, once a synapse changes strength, it is stable, and cannot be changed again for a while. How long this refractory period lasts is a great question, and could be a limiting factor in memory formation. I tried stimulating twice with an interval of 24 hours, but the slices got contaminated by bacteria.
A second interpretation is that structural plasticity is saturable. That is, the capacity for change has a limit at any given time point. Note that this does not really address the question of whether spine sizes are distributed over a continuous space, or discrete sizes.
What I really love about this experiment is how simple it is, and how many different directions you can go from here. And it was all enabled by new technology.
Monday, July 18, 2011
We Are All Obsolete
I've tried to keep this blog focused on academic science, but I've got an idea pinging around my head. I know it's not original (for one, my cousin mentioned it in a car ten years ago), but here it is: we are all obsolete. Every "successful" person today - whether they are a musician, scientist, programmer, or athlete - is going to be surpassed in our lifetime.
Historically, this is obvious. In athletics, records constantly fall. The average IQ scores which constantly go up. Groundbreaking papers are trivially reproducible.
Like dying, obsolescence happens slowly, so we can ignore it in our daily life. But it catches up to all of us.
There are a lot of factors pushing us towards obsolescence. We all lose intelligence as we age. There is the glacial force of natural selection (if those still work in an age of medicine and a social safety net). Today I'm going to focus on two factors that feed into this: the skyrocketing of the effective population; and the punctuated improvement of education.
Effective Population
I've had a fortunate education. I started at a Montessori elementary school, where play time motivated me to work hard. I went to a private high school that let me go to college a year early. Made up my own major, computational neuroscience, at Case Western. At Duke I was the first graduate student in a lab that eventually became a Howard Hughes lab. And now I'm in Geneva with free reign to do taste research in an effectively olfactory lab.
I got lucky. Lucky I was effectively an only child; that my parents were educated and valued education; that I generally had good teachers along the way. I'd guess only one in twenty Americans were as lucky as I was, but that could just be hubris.
When you think about the demographics of the world, it's easy to say that the US has 300 million people with the same opportunities, but that's not really the case. Some people estimate 25% of US children are in poverty. If you take inverse of that number, only about 225 million people in the US have ample opportunities; this is the US's effective population.
I got the idea of effective population from Information Processing. The idea is most applicable to a country like China, which has 1.3 billion people, but only about 300 million of them are able to compete in the global marketplace. That is, only 300 million have the nutrition, education, and financial stability to go to college, get educated, and try to create something in this world. The rest suffer from malnutrition, families that need the income, or simply from a lack of teachers necessary to educate a billion people.
If China's effective population is only 300 million, what about the rest of the world? I already estimated that the US's effective population is around 225 million. Rather than type this out, I'll just estimate the effective population of the world (gotta love a table that intimates billions of people don't exist; all of these numbers are pulled out of my ass. For example, how do I estimate Europe, which combines the well-developed West, and the still developing East?):
In total, about 1.2 billion, give our take a few hundred million. And this number is always going up.
A few pundits have made waves recently pointing this out (Hot, Flat, and Crowded; Post-American World). As a scientist this is both scary and exhilirating: the competition is going to get MUCH tougher; and hopefully the achievements will as well. But unfortunately, it means my effective place in the world will go down.
Educational Improvement
Some systems are so vast and hard to measure accurately that it's easy for anyone to have an opinion on how they should be run: health care; taxation; and for this post, education. Everyone has an idea how the education system should be run. School system funding should be ample, teachers should be held accountable, parents should read to their children, and the students themselves need to be measured (but we shouldn't teach to the test). We should teach people how to work together in groups, but not ignore basic skills. The subjects should include the three R's, but also newer things like psychology and computer programming. In the end, most people imprint on their own education, and have ideas about what did and did not work in theirs.
I have no idea how to improve education. But I do know the way we educate people now is vestigial and will be improved upon.
Right now, most education treats students like cogs in a factory (I generally sneer at those RSA videos as middlebrow, but holy cow that drawing struck a chord). We group people in classes because that's all we could do a century ago, if we wanted to educate as many people as possible. We continue doing so due to the inertia of institutions. And educational opportunities are almost non-existent for lower-class people, both in the US and around the world.
As I said, I don't know how to improve education, but there are lots of people trying different things. For example a multitude of individualized education programs are sprouting up (I am biased in favour of this, having started in Montessori school). There's the School of One in Brooklyn (more press here). The Gates Foundation is trying a lot of different models, supporting guys like Salman Khan. If one of these works, we can copy the model and disseminate it.
At the top of the post, I mentioned I had a good education, and that only one in twenty Americans might have had access to something like it. But the world is getting wealthier all the time, increasing the number of people who will get educated. And the education they're going to get continues to improve. It's easy to imagine thirty years from now, when I'm sixty, there will be a whole new generation of scientists, from around the world, that have a better education than me. I'm going to have to pit my ideas against theirs, hoping my experience can compensate. And eventually, I'm not going to even going to be proven wrong, I'm not even going to be able to compete.
Historically, this is obvious. In athletics, records constantly fall. The average IQ scores which constantly go up. Groundbreaking papers are trivially reproducible.
Like dying, obsolescence happens slowly, so we can ignore it in our daily life. But it catches up to all of us.
There are a lot of factors pushing us towards obsolescence. We all lose intelligence as we age. There is the glacial force of natural selection (if those still work in an age of medicine and a social safety net). Today I'm going to focus on two factors that feed into this: the skyrocketing of the effective population; and the punctuated improvement of education.
Effective Population
I've had a fortunate education. I started at a Montessori elementary school, where play time motivated me to work hard. I went to a private high school that let me go to college a year early. Made up my own major, computational neuroscience, at Case Western. At Duke I was the first graduate student in a lab that eventually became a Howard Hughes lab. And now I'm in Geneva with free reign to do taste research in an effectively olfactory lab.
I got lucky. Lucky I was effectively an only child; that my parents were educated and valued education; that I generally had good teachers along the way. I'd guess only one in twenty Americans were as lucky as I was, but that could just be hubris.
When you think about the demographics of the world, it's easy to say that the US has 300 million people with the same opportunities, but that's not really the case. Some people estimate 25% of US children are in poverty. If you take inverse of that number, only about 225 million people in the US have ample opportunities; this is the US's effective population.
I got the idea of effective population from Information Processing. The idea is most applicable to a country like China, which has 1.3 billion people, but only about 300 million of them are able to compete in the global marketplace. That is, only 300 million have the nutrition, education, and financial stability to go to college, get educated, and try to create something in this world. The rest suffer from malnutrition, families that need the income, or simply from a lack of teachers necessary to educate a billion people.
If China's effective population is only 300 million, what about the rest of the world? I already estimated that the US's effective population is around 225 million. Rather than type this out, I'll just estimate the effective population of the world (gotta love a table that intimates billions of people don't exist; all of these numbers are pulled out of my ass. For example, how do I estimate Europe, which combines the well-developed West, and the still developing East?):
Country/Continent | Population | Effective percentage | Effective Population |
USA | 300 million | 75% | 225 million |
China | 1.3 billion | 25% | 300 million |
India | 1.2 billion | 10% | 120 million |
Latin America | 600 million | 25% | 150 million |
Africa | 1.4 billion | 10% | 140 million |
Europe | 700 million | 60% | 420 million |
Asia ex-China/India | 1.5 billion | 10% | 150 million |
In total, about 1.2 billion, give our take a few hundred million. And this number is always going up.
A few pundits have made waves recently pointing this out (Hot, Flat, and Crowded; Post-American World). As a scientist this is both scary and exhilirating: the competition is going to get MUCH tougher; and hopefully the achievements will as well. But unfortunately, it means my effective place in the world will go down.
Educational Improvement
Some systems are so vast and hard to measure accurately that it's easy for anyone to have an opinion on how they should be run: health care; taxation; and for this post, education. Everyone has an idea how the education system should be run. School system funding should be ample, teachers should be held accountable, parents should read to their children, and the students themselves need to be measured (but we shouldn't teach to the test). We should teach people how to work together in groups, but not ignore basic skills. The subjects should include the three R's, but also newer things like psychology and computer programming. In the end, most people imprint on their own education, and have ideas about what did and did not work in theirs.
I have no idea how to improve education. But I do know the way we educate people now is vestigial and will be improved upon.
Right now, most education treats students like cogs in a factory (I generally sneer at those RSA videos as middlebrow, but holy cow that drawing struck a chord). We group people in classes because that's all we could do a century ago, if we wanted to educate as many people as possible. We continue doing so due to the inertia of institutions. And educational opportunities are almost non-existent for lower-class people, both in the US and around the world.
As I said, I don't know how to improve education, but there are lots of people trying different things. For example a multitude of individualized education programs are sprouting up (I am biased in favour of this, having started in Montessori school). There's the School of One in Brooklyn (more press here). The Gates Foundation is trying a lot of different models, supporting guys like Salman Khan. If one of these works, we can copy the model and disseminate it.
At the top of the post, I mentioned I had a good education, and that only one in twenty Americans might have had access to something like it. But the world is getting wealthier all the time, increasing the number of people who will get educated. And the education they're going to get continues to improve. It's easy to imagine thirty years from now, when I'm sixty, there will be a whole new generation of scientists, from around the world, that have a better education than me. I'm going to have to pit my ideas against theirs, hoping my experience can compensate. And eventually, I'm not going to even going to be proven wrong, I'm not even going to be able to compete.
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