Foo * bar1, * bar2;
note the extra star. Makes sense when you think about it.
Jon Hanley (Bristol): Actin dynamics in dendritic spines (Neural Dynamics Forum, 16/3/2012).
So there were lots of details in this interesting talk and lots of pictures of glowing dendritic spines; the basic topic was the changes that occur in dendritic spines during long term plasticity and the role actin plays. One thing I learned was that there are two processes at work in long term plasticity, a relatively quick one in which receptors are removed and sequestered away from the membrane and a slower one in which the spine itself changes size. Since, to the first approximation, the strength of the synapse depends on the number of receptors, the first process is sufficient to effect the plasticity: it would be interesting to know if the plasticity is somehow more provisional, more reversible, before the spine changes size and consolidates the change, I asked about this but it isn’t known. The spines themselves are not as button shaped as I expected, they are more like mushrooms, with a neck and a blob; the actin are long proteins that seem to lie along the inside of the spine and are continually being build and broken apart.
Fanny Monteiro (Bristol) Modelling diversity of marine phytoplankton (BCCS seminar Tuesday, 20/3/2012).
Phytoplankton play an important part of oceanic storage of carbon, they live near the ocean top, absorb carbon, die and sometimes sink to the depth where the carbon is sequestered. They come in diverse types, some with shells, shells of silica or calcium, some that can fix nitrogen. Originally modelling was done in a top down way, with different species examined in the lab and added to a simulation, now, a range of random artificial plankton are created with different efficiency and resource limitation trade-offs, grown in the simulated world oceans and then matched to known populations. This seems to work well, here it was used to examine distributions of nitrogen fixing plankton, the model is consistent with the limited experimental data, and predicts that unicellular nitrogen fixing plankton are more common and more significant than previously believed: the larger, colony based nitrogen fixing plankton are more common where there is more iron in the water, near deserts, and this is where data is more available. The distribution of nitrogen fixing plankton is explained by nitrogen availability, where it is available, usually because of up welling from the depths, they are out competed by other plankton species.
Margaret Coulvillon (Sussex): The hows and whys of bumble bee size variation (BCCS seminar Tuesday, 6/3/2012).
So bumble bees live in nests, like honey bees, who knew? Unlike other social insects, for bumble bees, there isn’t such a clear distinction between different roles, either by body type, or age. There is, however, a dramatic variation in size, the biggest bumble bees are ten times the size of the smallest. Now, bigger bumble bees are more commonly found foraging and smaller, tending to the young; there is no bimodal size split though. It seems by removing parts of the population it can be observed that bigger bees are better at foraging, surprisingly though, they are also better at looking after the nest. Why the range of sizes then: it turns out that smaller bees survive starvation better so the fitness and robustness curves slope opposite ways with bee size and rather than choosing a sweet spot, it is better to have a variation so some bees survive protracted starvation, by tending the young they ensure the nest’s genetic material will survive.
Bees laid closer to the center of the nest get fed more before they emerge and end up bigger. The egg location depends on the apparently random peregrination of the queen. I wondered if the balance is maintained by always having the same laying behavior or if the queen steers it to the ideal by modifying her laying behavior based on local cues. This isn’t known.
This leaves out lots of the great bumble bee details in the talks and the description of the many experiments on nests to work out what is described here and to rule out other hypothesizes about the bee size.
I spoke about jitter again at the Neurodynamics Forum on the 2 March 2012. There was a huge amount of discussion, with a few people asking if a jitter based measure could be used to assess learning or to study disease.
I have started a project on sourceforge
to store the c++ spike train metric code that was used for the paper with Thomas Kreutz:
On the efficient calculation of van Rossum distances.
The van Rossum metric measures the distance between two spike trains. Measuring a single van Rossum distance between one pair of spike trains is not a computationally expensive task, however, many applications require a matrix of distances between all the spike trains in a set or the calculation of a multi-neuron distance between two populations of spike trains. Moreover, often these calculations need to be repeated for many different parameter values. An algorithm is presented here to render these calculation less computationally expensive, making the complexity linear in the number of spikes rather than quadratic.
The paper is still under review.
The mathematician and Fields medal winner Tim Gowers is calling for a boycott of Elservier:
and there is a web site
to sign up, as you should! One question though: should Springer be boycotted too?
I spoke to Mathematics in NUI Galway on the 19 January 2012 about variation in spike times. It was a great seminar in that most of the department seems to have turned up, I love departments that have a tradition where everyone goes to the main departmental seminar even if the field is very different from their own. The questions were very good too; there was a discussion of the algorithm for calculating the Victor-Purpura metric, there was a suggestion of a method which was linear in the number of spikes, rather than quadratic (*). Another question asked whether the algorithm had been tested by putting jitter in artificially to a single spike train and then testing to see if the artificial jitter could be recovered. I answered that there is no good model for adding noise to a spike train, in fact, that’s something we are thinking about; but it is likely we will be asked to do something like this by the referees for the paper.
Of course, Galway is my alma mater, which made the talk extra fun.
(*) The idea is that you would choose one spike train and look at spikes from the other were within 2/q of it, with some protocol based on proximity for decided which spikes to pair up if this was ambiguous.
Most of the data I use is recorded from the neurons in Zebra finch, an Australian song bird often used as a model animal for learning. The particular value of using song bird in studying primary processing of sensory information is that a corpus of songs from the same species is considered a good proxy for the natural acoustic environment. It is important to use natural stimuli, neuronal processing is likely to be tuned to natural stimuli and processing is likely to exploit the statistical structure of the environment. However, what is meant by natural is hard to define, we get away with not thinking about this by pretending a collection of bird songs will do, this isn’t proved, but it is plausible.
Anyway, above is a recording of a Zebra finch singing, first a series of squawks, announcing the song, then the song itself.