Posts Tagged behaviour

Internet birdfest Fabiana Kubke Jun 01

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A few days ago I got an email from a colleague of mine pointing me to a video about birds of paradise. I am happy I went and looked at it because it is quite amazing. There is no question why this group of birds stand apart from others – they are not beautiful to watch, but their behaviour, too, is quite amazing. Watch:

There are other birds that I find absolutely amazing. The Lyrebird for example, incorporates into its song sounds that it hears as it goes about life. There are two types of song learning birds (songbirds). Some will learn to imitate a song from an adult tutor as they are growing up, and pretty much sing that song as adults. Others can continue to incorporate elements to their song as adults. The lyrebird falls into this last group. But what I find amazing about the lyrebird is not that it incorporates new song elements, but that some of those sounds are not “natural” sounds. Watch:


Another amazing bird is the New Caledonian crow. A while back Gavin Hunt (now at the University of Auckland) came to find out that these birds were able to manufacture tools in the wild. They modify leaves and twigs from local plants to make different types of tools which they then use to get food. This finding spurred a large body of work on bird intelligence. Watch:

And if you are interested of where these wonderful animals all came from, there is a fantastic blog by Ed Yong over at national Geographic. Read:

The changing science of just-about-birds and not-quite-birds
(HT @BjornBrembs)

I am sure there is a screenplay somewhere in there, inspired by Tron and involving cats chasing birds in cyberspace. But I shall leave that for someone more creative than me.

That pesky BRAIN Fabiana Kubke May 04

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When a President annouces a scientific project as publicly as President Obama did, the world listens. The US is planning to put signifcant resources behind a huge effort to try to map the brain. There has been a lot said about this BRAIN project [1], and I have been quietly reading trying to make sense of the disparate reactions that this ‘launch’ had – and trying to escape the hype.

Sir Charles Bell (1774-1842). CC-BY-NC Wellcome Library, London

I can understand the appeal – the brain is a fascinating invention of nature. I fell in love with its mysteries as an undergraudate in Argentina and I continue to be fascinated by every new finding. What fascinates me about the discipline is that, unlike trying to understand the kidney for example, neuroscience consists of the brain trying to understand itself . That we can even ask the right questions, let alone design and perform the experiments to answer them is what gets me out of bed in the morning.

Trying to understand the brain is definitely not a 21st Century thing.  For centuries we have been asking what makes animals behave the way they do.  And yet we still don’t really know what it is about our brains that makes us the only species able to ask the right questions, and design and perform the experiments to answer them?

Many of us neuroscientists might agree that how we think about the brain came about from  two major sets of finding. Towards the end of the 19th Centrury it finally became accepted that the brain, like other parts of the body, was made up of cells. It was Santiago Ramon y Cajal’s tireless work (with the invaluable assistance of his brother Pedro) that was fundamental in this shift. This meant that we could apply the knowledge of cell biology to the brain. The second game changer was the demonstration that neurons could actively produce electric signals. In doing so, Hodgkin and Huxley beautifully put to rest the old argument between Volta and Galvani. This meant we had a grip on how information was coded in the brain.

CC-BY kubke

From this pioneering work, neuroscience evolved directing most of its attention to the neurons and their electrical activity. After all, that is where the key to understanding the brain was supposed to be found. Most of what happened over the twentieth century was based on this premise. Neurons are units that integrate inputs and put together an adequate output passing the information to another neuron or set of neurons down the line until you get to the end. In a way, this view of the brain is not too different from a wiring diagram of an electronic circuit.

Trying to understand the wiring of the brain, however, is, not easy. There are thousands and thousands of neurons each with a multitude of inputs and outputs. You can quickly run out of ink trying to draw the wiring diagram, It is because of this complexity that neuroscientists (just like scientists in many other disciplines) turn to simpler models. We have come to know some secrets about learning from studying the sea slug Aplysia, about how the brain gets put together from flies and frogs, and even about how neurons are born in adult brains from singing canaries. What all these models have in common is that we can tie pretty well a very specific aspect of brain function to a circuit we can define rather well. And we have learned, and keep learning, heaps from these models. The main thing we learn (and the reason why these models continue to be so useful and fundamental for progress) is that the ‘basics’ of brains are quite universal – and once we know those basics well, it is a lot easier to work out the specifics in more complex brains.

Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biology Vol. 6, No. 7, e159 (CC-BY)

Trying to understand the architecture of circuits has proven to be of major value (and this is what the connectome is about). But building the connections is not just about drawing the wires – you need to build in some variability – some connections excite while others inhibit, some neurons respond in predictable linear ways, others don’t.  And when you are done with that, you will still need to start thinking about the stuff we have not spent a lot of time thinking about: those other cells (glia) and the stuff that exists in between cells (the extracellular matrix). More and more, we are being reminded that glia and extracellular matrix do more than just be there to support the neurons.

So it is not surprising to find some skepticism around these large brain projects. Over at Scientific American, John Hogan raises some valid criticisms about how realistic the ambitions of these projects are given the current state of neuroscience (read him here and here). Other lines of skepticism center around the involvement of DARPA in the BRAIN project (read Peter Freed’s views on that here or Luke Dittrich’s views here). Others criticize the lack of a clear roadmap (read Erin McKiernan’s views here). Others have expressed their concerns that too strong expectations on advancing our knowledge of the human brain will overlook the importance of exploring simpler circuits, something that had been stated clearly in the original proposal [2].

Is now the right time?

Back in the ‘90’s the decade of the brain had insinuated it would solve many of these problems, I don’t think it did. Despite the neuroscience revolution from about a century ago and the work that followed, we still have not been able to solve the mysteries of the brain.

But this decade is somewhat different. I am reading more and more stuff that has to do with the emergent properties of the brain – not just the properties of the neurons. And for the first time since I started my road as a neuroscientists I am being able to ask slightly different questions. I did not think that successful brain machine interfaces would be something I’d get to see in my lifetime. And I was wrong. Even less did I think I would get to see brain to brain interfaces. But the works is moving forward there too.

The BRAIN project is not alone. In Europe the Human Brain Project received similar attention. We all expect that such boosts in funding for multidisciplinary research will go a long way in making things move forward.

It is inevitable to think of the parallels of the approach to these Big Brain projects and the National Science Challenges – which are wonderfully expressed by John Pickering here.

I think that Erin McKiernan’s cautionary words about the BRAIN project might be quite appropriate for both:

Investing in neuroscience is a great idea, but this is not a general boost in funding for neuroscience research. This is concentrating funds on one project, putting many eggs in one basket.

[1] Brain Research through Advancing Innovative Neurotechnologies,
[2] Alivisatos, A. P., Chun, M., Church, G. M., Greenspan, R. J., Roukes, M. L., & Yuste, R. (2012). The Brain Activity Map Project and the Challenge of Functional Connectomics. Neuron, 74(6), 970–974. doi:10.1016/j.neuron.2012.06.006

Science lessons from 8 year old children Fabiana Kubke Dec 22

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[Cross posted from Talking Teaching]

Ed Yong in Not Exactly Rocket science alerted me to an article published in Biological Letters Biology Letters from the Royal Society. I will not discuss the content of the article, Ed Yong has (as usual) done a wonderful job. I would like instead to share the ‘concept’ of the article.

The article reports on some research that shows that bumble-bees use both colour and spatial relationship in their foraging behaviour. But enough about that. What is unique about this article is that the research was conducted by a group of school children. It is also unique in that it is written by a group of school children (in their language). And the icing on the cake are the figures: pencil coloured; no fancy graphic software.

This is, in my opinion, authentic teaching at its best. And authentic learning. And while we are at it, authentic publishing.

So what have I learned from this group of children? That, as they say, science is fun. And that teaching science, whatever the student age group, can be made fun and authentic and can get children motivated.

The background reads:

Although the historical context of any study is of course important, including references in this instance would be disingenuous for two reasons. First, given the way scientific data are naturally reported, the relevant information is simply inaccessible to the literate ability of 8- to 10-year-old children, and second, the true motivation for any scientific study (at least one of integrity) is one’s own curiosity, which for the children was not inspired by the scientific literature, but their own observations of the world.

I could not agree more. I love biology because I ‘played’ with biology as a child. I was fortunate enough to have a father who never answered my question with ‘I don’t know’ without following that up with ‘but lets try to find out’. As a child my father valued my questions and my curiosity, more so about things he didn’t have an answer for. And I will always be grateful to him for that. For my teachers, well, that was a different issue: rather annoying having a pupil in the class that just refused to overcome the ‘why?’ stage.

And these children have been given a great gift by being it let known that their thoughts and ideas have value. And that, once that barriers that have to do with the specific language of the scientific literature are withdrawn, their ideas and thoughts can bring about new knowledge.

These children will also grow up having learned a few fundamental things about science: How an idea is brought into shape, how scientific questions are narrowed, and the hard work and discipline that is needed to see an experiment through. Oh yes, and that no matter how good an idea may be, reviewers may still reject your grant.

None of this they could have learned from a science textbook.

The editors of the Royal Society should also be commended for not requiring that the manuscript adjust to the traditional publishing formats and allowing the authentic voice of the children to come through. This paper should become obligatory reading in science classes. If nothing else, children will recognise their own voices and curiosity in the reading, and, who knows, other groups of children with innovative teachers may teach us (adult scientists) another thing or two.

P. S. Blackawton, S. Airzee, A. Allen, S. Baker, A. Berrow, C. Blair, M. Churchill, J. Coles, R. F.-J. Cumming, L. Fraquelli, C. Hackford, A. Hinton Mellor1, M. Hutchcroft, B. Ireland, D. Jewsbury, A. Littlejohns, G. M. Littlejohns, M. Lotto, J. McKeown, A. O’Toole, H. Richards, L. Robbins-Davey, S. Roblyn, H. Rodwell-Lynn, D. Schenck, J. Springer, A. Wishy, T. Rodwell-Lynn, D. Strudwick and R. B. Lotto (2010) Blackawton bees. Biology Letters DOI:10.1098/rsbl.2010.1056

For fireflies, getting the girl requires team work Fabiana Kubke Jul 10

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ResearchBlogging.orgImagine you drive into a motel in Gatlinburg TN, and see behind an open room door 2 guys setting up cameras pointing at the beds while two young women peek from the parking lot. Well, if it was in the mid ’90′s it might have been Drs Moiseff and Copeland setting up the equipment before venturing into Elkmont in the Smoky Mountains to study the local fireflies. (And one of the two women would have been me.)

Andy Moiseff and Jon Copeland started studying the population of fireflies in the Smoky Mountains National Park after learning from Lynn Faust, who had grown up in the area, that they produced their flashes in a synchronous pattern.

Image courtesy of Andy Moiseff

In the species they are studying (Photinus carolinus) the males produce a series or bursts of rhythmic flashes that are followed by a ‘quiet period’. But what is particularly interesting about this species is that nearby males do this in synchrony with each other. If you stand in the dark forest, what you see is groups of lightning bugs beating their lights together in the dark night pumping light into the forest in one of nature’s most beautiful displays.

Females flash in a slightly different manner and, as far as I know, they don’t do it synchronously either with other females nor with the males. One interesting thing in Elkmont is that there are several species of fireflies, and you can pretty much tell them apart by their flashing patterns. But as useful as this is for us biologists (since it avoids having to go through extensive testing for species determination), the question still remained of whether the flashing patterns played a biological role.

And this is what Moiseff and Copeland addressed in their latest study published in Science. They put females in a room where LEDs controlled by a computer simulated individual male fireflies. The LEDs were made to flash with different degrees of synchronisation and they looked at the responses of the females. They found that while the females responded to synchronous flashes of the LEDs, they really didn’t seem to respond when the flashes were not synchronous. Even more, they responded better to many LEDs but not much to a single one. What this means, is that if you are a male of Photinus carolinus, you better play nice with your mates if you want to get the girl.

What *I* want to know is how this behaviour is wired in the brain. At first hand, this seems like a rather complex behaviour, but in essence all that it seems to require is a series of if/then computations, which should not be too hard to build (at least not from an ‘electronic circuit’ point of view). But Bjoern Brembs reminded me of a basic concept in neuroscience: brains are evolved circuits, not engineered circuits. So, Andy and Jon, how *do* they do it?

Original article: Moiseff, A., & Copeland, J. (2010). Firefly Synchrony: A Behavioral Strategy to Minimize Visual Clutter Science, 329 (5988), 181-181 DOI: 10.1126/science.1190421

Brain day, bigger, longer, uncut Fabiana Kubke Mar 20

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If you have been paying attention, you might have been hearing a rise in stories related to brains in the media (I will be blogging about some of them soon). This is because this has been Brain Awareness Week. My first (ever) post on a blog (now defunct and reborn here) was indeed one describing my last year’s experience organizing for the 3rd time the Open Brain Day at the University of Auckland.

A year has gone by, and I am sitting at this year’s Brain Day that is being held at the Business School’s Owen Glenn Building at the University of Auckland. This year we are also celebrating the launch of the Centre of Brain Research, which launched towards the end of last year, finally replacing the Auckland Neuroscience Network.

For the first time, I can look at the day without the pressure of running after a myriad of details. And this year we are bigger, longer and uncut. (Well, the latter not so true since we have some cut brains to show you what they look like on the inside).

If you have come to brain open days before, check it out again. If you haven’t then this would be a great time to start.

[Open] Science Sunday — 28.2.10 Fabiana Kubke Feb 28

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I am not sure why, but this week appeared to be filled with news about science to share. All of these are brought to you by the magic of Open Access or the efforts of people in the web to make science accesible to everyone.

I would normally not include articles published in Nature here, but this week David Winter from The Atavism pointed me to this one: “Complete Khoisan and Bantu genomes from southern Africa” by Stephan C. Schuster and a group of collaborators. The authors open their paper stating that

’The genetic structure of the indigenous hunter-gatherer peoples of southern Africa, the oldest known lineage of modern human, is important for understanding human diversity.’

The study has been published under a creative commons licence ( and the data has also been released here. I dont know whether Nature will ever move to a full Open Access format, but I think it is worth acknowledging that at least some of their material is made available withouth a subscription. To read a full review of the article, you can visit David Winter’s blog.

PLoS One (which yes is a  fully Open Access journal) published an article on the cognition behind spontaneous string pulling in New Caledonian Crows, by Alex Taylor, Felipe Medina, Jennifer Holzhaider, Lindsay Hearne, Gavin Hunt, and Russell D. Gray. New Caledonian crows are better known for their ability to manufacture tools both from materials that they would normally find in the environment as well as some they would not. New Caledonian crows can solve rather complex puzzles, and for the most part, it has been assumed that this reflected some ‘higher’ cognitive ability that require building a cognitive scenario and imagination. In this study, the authors subjected crows to a series of tests, and conclude that:

Our findings here raise the possibility that string pulling is based on operant conditioning mediated by a perceptual-motor feedback cycle rather than on ‘insight’ or causal knowledge of string ‘connectivity’.

As usual, things are not so black and white. You can read the details of the study on the PLoS One site, and Wired Science has a great review by Brandon Keim on the paper.

Research Blogging Awards 2010And, if you are looking for interesting blogs filled with science nerd content, then this is the best time to find them.

The finalists for best of Research Blogging are out and there is no shortage of interesting stuff to look into. Also out is the Open Laboratory 2009. This is a great collection of science blog posts that is really worth your money. So go on now, go get yourself a copy…

The Open Laboratory 2009

And if all that geekiness was still not enough, then you are in luck.

Next week will see Global Ignite Week: Ignite talks in 65 cities and 5 continents (and yes, there is one in Wellington on Tuesday). Ignites are a great presentation format (well, unless you are a speaker since they are really really hard to do well!).  If you have not heard one before, there are plenty on YouTube Ignite Channel.

If you cannot make it to Wellington, then Auckland will be having on Thursday another Late at the Museum, this time on innovate science. I know, I am now sounding like a tourist guide.

One more (an last). If you want to know everything there is to know about <ahem!> me :), thanks to the magic of Bora Zivkovic and The Blog Around the Clock, now you can. There should be a warning or disclaimer before I lead you to this link.

Full Disclaimer: I am an academic editor for PLoS One and I collaborate with the group behind the New Caledonian Crow Study

Getting up to speed with sound localisation Fabiana Kubke Feb 25

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ResearchBlogging.orgFunny how we are really good, for the most part, at knowing where sounds are coming from. And it is funny since the ear provides the brain with no direct information about the actual relationship in space of different sound sources. Instead, the brain makes use of what happens to the sound as it reaches both ears by virtue of, well, being a sound wave and that we have two ears separated in space.

Imagine a sound coming from the front, the sound will arrive to the two ears at the same time. But if it is coming from the right it will arrive to the right ear first, and to the left ear a wee later. This ‘time difference‘ will depend on the speed of sound in air and how far apart our ears are. Even more, as the sound source moves from the far right to the front of the head those time differences will become smaller and smaller, until they are zero at the front. If one could put one microphone in each ear, one could reliably predict where the sound comes from by measuring that time difference. And this is exactly what a group of neurons in the brain does.

Easy enough? Not quite.

The way the brain works is that things on the left side of our body are mapped on the right side of our brains, and things on the right side of our bodies are mapped on the left side of our brains. So the ‘time comparison’ neurons on the right side of the brain deal mainly with sound from coming from the left (and neurons dealing with the sound from the right are on the left side of the brain). But to do the time comparison these neurons need to get the information from both ears, not just from only one side!

Figure 1 (by Kubke CC-BY)

This raises this conundrum: the neural path that the information from the left ear needs to travel to get to the same (left) side of the brain will inevitably be shorter than the path travelled by information coming from the other side of the head. So how does the brain overcome this mis-match?

And here is where having paid attention at school during the  “two trains travelling at the same speed leave two different stations blah blah blah” math problem finally pays off. When a sound comes from the front, the information arrives to each of the ears at the same time. The information also arrives to the first station in the brain (nucleus magnocellularis) at the same time. But time comparison neurons need information from both ears, and the path that the information needs to travel from the right side to the time comparison neurons in nucleus laminaris on the left side (red arrow in figure 1) is longer than the path from the same side (blue arrow in figure 1).

However, when you look into an actual brain, things are not so straight-forward (sorry for the pun). The axons from nucleus magnocellularis that go to the time comparison neurons on the same side of the brain take a rather roundabout route (as in figure 2). And for long we assumed that such roundabout way was enough to make signals from the left and right sides to arrive at about the same time.

Figure 2 (by Kubke CC-BY)

Easy enough? Not quite

When Seidl, Rubel and Harris actually measured the length of the axons (red and blue) they found that there was no way that the information could arrive at about the same time and that the system could not work in the biological range. But this problem could be overcome (back to the old school problem) by having the two trains (action potentials rather) travel at different speeds. And this is something that neurons in the brain can relatively easily do in two ways: One is to change the girth or diameter of the axon. The other is to regulate how they are myelinated. Myelin forms a discontinuous insulating wrap around the axon, which is interrupted at what is called the Nodes of Ranvier. The closer the Nodes of Ranvier are, the slower the action potential travels down the axon.

What the group found was that both axon diameter and myelination pattern were different in the direct (blue) and crossed (red) axons. When they now calculated how long it would take for the action potential from both sides to reach the time comparison neurons in nucleus laminaris, adjusting speed for the differences in the two axons, they found that yup, that pretty much solved the problem.

Easy enough? Quite

Like the authors say:

The regulation of these axonal parameters within individual axons seems quite remarkable from a cell biological point of view, but it is not unprecedented.

But remarkable indeed, considering that this regulation needs to adjust to a very high degree of temporal precision. I have always used the train analogy when I lecture about sound localisation, and always assumed equal speed on both sides. Seidl, Rubel and Harris’ work means I will have to redo my slides to incorporate differences in speed. Hope my students don’t end up hating me!

Seidl, A., Rubel, E., & Harris, D. (2010). Mechanisms for Adjusting Interaural Time Differences to Achieve Binaural Coincidence Detection Journal of Neuroscience, 30 (1), 70-80 DOI: 10.1523/JNEUROSCI.3464-09.2010

[Open] Science Sunday — 20.12.09 Fabiana Kubke Dec 20

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Great things to share this Sunday thanks to the magic of the internet and open access….

The good

There are some good news around Open Access:

First, last week Nat Torkington alerted me of this link. The first paragraph of the summary states

’With this notice, the Office of Science and Technology Policy (OSTP) within the Executive Office of the President, requests input from the community regarding enhancing public access to archived publications resulting from research funded by Federal science and technology agencies.’

Some commenting around this issue can be found in the Office of Science and Technology Policy Blog. (via @BoraZ on twitter). It is great to see the OSTP having started this discussion, and I will be interested to see where this leads to.

With the year coming to an end, nothing like summarizing what has been achieved, and here is a post summarizing how 2009 was a great year for Open Access (also via @BoraZ)

And it was great to hear about the new partnership between and PLoS. Nice!

The ‘How is this Reasonable?”

There is a post by Martin Fenner describing a talk on Open Access he gave at his University. I especially liked this extract:

’Reuse of a figure or table in an academic seminar usually falls under fair use, but many journals still require a (free) permission. And using the same figure in a medical conference can cost several hundred dollars, and it doesn’t really matter that you are one of the authors of the paper’

I did not know that use of my own figures at a conference did not fall under fair use. It’s just not right.

But this is even worse:

Who could oppose non-profit blind/disabled groups helping disabled people get access to written work?

You can find the answer in BoingBoing.

Back to the good: Cornell University

Cornell University Library partners with the Internet archive (heard through Open Access News). Absolutely priceless gems can be found here! There is nothing like dusting off the cobwebs of some old journal issues and reading the scientific discoveries as they were described originally by the scientists themselves. Cornell University has made this a lot easier.

Cornell University has a great series of videos on YouTube, including a really interesting one on bird’s songs. (By the way, wonderful description of feathers in Ed Yong’s Not Exactly Rocket Science blog.)

The Cornell lab of Ornithology also runs a great Citizen Science programme. (Dave Munger has a wonderful post about Citizen Science in Seed Magazine.)


OK, granted, I didn’t get this tweet from @sciencebase this week, but it is really so worth it! So, if you are not up to becoming a citizen scientist you might still be up for some quirky science party tricks. (If you like this video, there is more at Richard Wiseman’s Blog)

The ever-changing world of dendritic spines Fabiana Kubke Dec 18

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ResearchBlogging.orgSantiago Ramón y Cajal originally described spines in the dendrites of neurons in the cerebellum back in the late 19th century, but it wasn’t until the mid 1950’s with the development of the electron microscope that these structures were shown to be synaptic structures. Although it has been known that the number of dendritic spines changes during development and in association with learning, most studies have inferred the changes by looking at static time points rather than monitoring individual spines in the same animal over time, partly, due to the difficulty of tracking a single structure of about 0.1 micrometer in size (0.0001 mm). But new advances in imaging technology have allowed researchers to ‘follow’ individual spines over time both in vitro and in the whole animal.

Purkinje Cell by S Ramon y Cajal

Dendritic spines are no longer thought of as the static structures of Ramón y Cajal’s (or even my) generation, but rather dynamic structures that can be added and eliminated from individual dendrites. And because each spine is associated with a synaptic input, and because their structure and dynamic turnover is known to have a profound effect on neuronal signaling, one cannot but be tempted to propose that they are associated with specific aspects of memory formation.

Two developments have made it possible to monitor individual dendritic spines at different time points in the same animal: the ability to incorporate fluorescent molecules into transgenic mice that make the spines visible under fluorescent illumination, and the development of in vivo transcranial two photon imaging that allow researchers to go back to that individual dendrite and monitor how the dendritic spines change over time. Two papers published in Nature make use of these techniques to look at how dendritic spines change in the motor cortex of mice that have learned a motor task.

In one, Guang Yang, Feng Pan and Wen-Biao Gan looked at how spines changed when either young or adult mice were trained in to learn specific motor strategies. They observed that spines underwent significant turnover, but that learning the motor task increased the overall number of new spines and that a small proportion of them could persist for long periods of time. They calculated that although most of the newly formed spines only remained for about a day and a half, a smaller fractions of them could still persist for either a couple of months or a few years. Based on their data they suggest that about 0.04% of the newly formed spines could contribute to lifelong memory.

Dendritic spine by Tmhoogland

Another study by Tonghui Xu, Xinzhu Yu, Andrew J. Perlik, Willie F. Tobin, Jonathan A. Zweig, Kelly Tennant, Theresa Jones and Yi Zuo did a similar experiment, but using a different motor training task. Like the Yang group, they also saw that training leads to both the formation and elimination of spines. Although newly formed spines are initially unstable, a few of them can become stabilized and persist longer term. Further, training made newly formed spines more stable and preexisting spines less stable. The authors interpret their results as an indication that during learning there is indeed a ‘rewiring’ of the network and not just addition of new synapses.

The two papers were reviewed by Noam E. Ziv & Ehud Ahissar in the News and Views section. Here they raise the issue that, if such a small number of spines are to account for the formation of stable memories, then what are the consequences of the loss of a somewhat larger number of spines on the neuronal network?

For someone like me that more than once as an undergraduate used a microscope fitted with a concave mirror to use the sunlight to illuminate the specimen, the ability to monitor individual synaptic structures over time in a living organism can only be described as awesome. But, as pointed out by Ziv and Ahissar,

’[…] although it remains to be shown conclusively that these forms of spine remodeling are essential components of long-term learning and not merely distant echoes of other, yet to be discovered processes, these exciting studies make a convincing case for a structural basis to skill learning and reopen the field for new theories of memory formation.’

Yang, G., Pan, F., & Gan, W. (2009). Stably maintained dendritic spines are associated with lifelong memories Nature, 462 (7275), 920-924 DOI: 10.1038/nature08577
Xu, T., Yu, X., Perlik, A., Tobin, W., Zweig, J., Tennant, K., Jones, T., & Zuo, Y. (2009). Rapid formation and selective stabilization of synapses for enduring motor memories Nature, 462 (7275), 915-919 DOI: 10.1038/nature08389
Ziv, N., & Ahissar, E. (2009). Neuroscience: New tricks and old spines Nature, 462 (7275), 859-861 DOI: 10.1038/462859a

Getting the timing right for song control Fabiana Kubke Dec 11

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ResearchBlogging.orgSongbirds have evolved special areas in the brain that are used for song learning and song production. Two types of output connections from a cortical area known as HVC (proper name) each go to two ‘separate’ pathways. Some HVC neurons connect directly with neurons in a brain area called RA (robust nucleus of the archopallium), which in turn connects with the motoneurons that control the muscles in the vocal control organ (syrinx). Another set of HVC neurons connect through what is called the anterior forebrain pathway, a collection of cortical, thalamic and basal ganglia nuclei that are important for birds to learn their song. The two pathways talk to each other through a nucleus called LMAN that sends a direct input to RA.

Vocal circuit, by Kubke

The anterior forebrain pathway sends an error signal through the connections from LMAN to RA to ultimately control the motoneurons in nXIIts to produce the desired song structure. What is puzzling about the circuit is how the precise timing for this to operate efficiently is achieved. Because it takes time for the action potential to travel down the axon, and because it takes time for information to travel through synapses, the anterior forebrain pathway roundabout way (HVC-to-X-to-DLM-to-LMAN-to-RA) should be much slower than the speed of travel of information from HVC to RA. And this is precisely what Arthur Leblois, Agnes Bodor, Abigail Person and David Perkel examined.

To determine this, they electrically stimulated HVC and recorded from area X, DLM and LMAN, and were able to explore the mechanisms by which information travels around the anterior forebrain pathway as well as how long it takes to get from one point to another (latency).

How is transmission routed along the anterior forebrain pathway?

What they found is that low intensity stimulation from HVC produces excitation of area X neurons, but that higher intensity stimulation also produces a rapid inhibitory input from local area X circuits. One of the effects of this early inhibition is a lengthening of the time interval between consecutive action potentials in the neurons in area X that project to DLM (pallidal neurons).

DLM is normally inhibited by pallidal neurons in area X. But if the time interval between action potentials in the pallidal neurons is increased, it releases the ‘veto’ signal on DLM neurons which can then fire action potentials (either in response to other excitatory inputs or as a result of ‘post inhibitory rebound’). Based on the results, DLM neurons will therefore become activated (and in turn activate LMAN) when the local inhibition in area X (in this case triggered by HVC stimulation at high intensity) lengthens the time period between action potentials in the pallidal neurons. This is consistent with the observation that responses in LMAN could only be elicited by high levels of stimulation in HVC.

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In this way, an input from HVC sufficient to elicit fast inhibition in area X, removes the veto signal on neurons in DLM, which are then able to discharge and excite LMAN, which can then send the appropriate signals to RA.

Does the timing work?

The short answer is yes. First, the authors showed that although the path-length between HVC-Area X and that of Area X-DLM, are similar the conduction times are much smaller in the latter. This, they suggest, is achieved both by an increase in diameter of the axons projecting from AreaX to DLM, axons which are myelinated even within DLM. The population latency in DLM and LMAN following HVC stimulation is very similar, but the authors argue that perhaps the population of DLM neurons that have the shortest latencies that are the ones that are playing the key role.

The specialisations in axonal morphology and myelination of the pallidal neurons may be an evolutionary adaptation that contributes to a short latency pathway that can modulate fine temporal features of song production.


Leblois, A., Bodor, A., Person, A., & Perkel, D. (2009). Millisecond Timescale Disinhibition Mediates Fast Information Transmission through an Avian Basal Ganglia Loop Journal of Neuroscience, 29 (49), 15420-15433 DOI: 10.1523/JNEUROSCI.3060-09.2009

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