Revell Without a Cause

(Reposted here by request)

To answer the question “what’s it all for?”, I think it is too simplistic to denote one reason – the creators of the group had theirs, I have mine, and the other 174997 people have theirs.

I can only tell you why I support it; I resent Pop[ular] music being presented and covered as if that is the entire scope of musical talent.

Sure some contestants may have real talent, sure they may deserve a record contract – but the extent to which the market is manufactured, manipulated and subverted, and quite frankly hi-jacked (X-Factor will try to claim the xmas no1 prize every year it runs) sickens me. The result is that other talented artists in other genres and tastes can never get a look in. Even if Joe does overtake RATM to become no1 it has made an impact, and it has also been fun – so we all win!

As for X-Factor [et al] itself, I believe the entire format to be based around exploitation, misinformation and lies:

- the contestants with no talent are exploited; they are just there for us all to laugh at and feel superior.

- the real contestants are exploited; the sheer amount of overemotional clips and sound bites derived from their interim activities gives the show producers hours of footage to pack out an hour on tv for virtually nothing.

- the winner is exploited; £1m record contract? T&Cs apply I bet you! Not one finalist from these shows has gone on to have a truly transcended music career, most only last a year, why? Because their fame only lasts as long as the X-Factor machinery holds them in the spot light. Says a lot about their talent if thats all it takes for the public to lose interest.

- and finally, the audience and viewers are exploited; they are sucked in by cheap laughs at the talentless entries, hooked by the fake sugary compliments thrown at those who do have talent, kept engrossed by the “emotional” rollercoaster of their X-Factor experience, and so after weeks and weeks of this, are so convinced that this is the epitaph of quality music, they go and buy the single – especially as it makes a marvellously convenient and easy Christmas gift!

Phew! That was a biggie *rant over*

After a few snags with a dependency that did not want to play nicely, I managed to get the MIIND simulator installed. This is required for the next step of the project.

I now need to demonstrate that as the mu increases (which we can now predict reasonably accurately), the output firing rate steadily increases. This produces a sigmoid shape on a histogram… after a fashion!

Today I ran the one population model [to test, we need to do the same with a two population model], with a variety of different rates, which equates to different mu values. The mu can also be varied by altering the synaptic weight as well, although this would be much less significant changes, and should in theory result in exactly the same curve.

Once I have done this in NEST I will need to replicate it in MIIND

Since last week I managed to track down and resolve the problems I had been having with help from Marc. It turned out to be a quota problem; the data exceed my quota and thus overwrote parts of itself. I thought I had modified my script to do this, but evidently not. Once I had got my script using /tmp/ (and my portable hdd) instead of my home folder, everything works fine again.

I now have a working, one population model. The input and output is consistent as far as I can tell. This is something that is bothering me though, I don’t feel like I am understanding the theory as well as I should. It is certainly making my progress slow, which is frustrating. I am trying to make sense of the Amit & Brunel papers, but they’re big papers and it is all too easy to get lost n the text.

A firm grasp on the background is becoming most apparent now that I have started my final report. I will not be able to write up that section well at all unless I do.

Yesterday was not a good day for my project; I found myself being propelled backwards from the doorstep of phase 2, ending up at the gates of phase 1, right where I started…. well sort of. It certainly felt that way.

The most significant acheivement I have made som far is confirming that I can correctly predict the properties of the output for a neuron population. The next stage is to use models with multiple populations. Before I could move on to this stage, I needed to remind myself of the tools that I had been using before.

The way to do this was to repeat the post-processing of the simulated output for the single population model. As the output files are mammoth in size, I can only store them in /tmp/. This means I had to run the simulation again as well.

Something is not right. While the size of the file is almost identical, the order of magnitude of spikes is correct, my post-processing tool was failing. Given that the output for any given model should be identical no matter how many times it is run, and that the tool worked before, this should not happen. I have modified the tool to cope with the error and produce the meaningful output.

The output I now get still fits the theoretical predictions, and so is correct. However, it is most disconcerting that it is not identical as it should be. I have yet to establish what exactly causes the NEST simulator to behave differently, or if the annomolies are due to the corrupt file. Even if that is the case, I do not know what is causing that to happen.

I will definately be mentioning this to Marc in our next meeting.

So far, I have sucessfully managed to implement a one populatuion model and estimate that its behaviour is consistent. I have done this by measuring the spikes generated by the poisson generators (input of the population) and the spikes generated by the population itself (output of the population).

The results from the latter step need to be checked against theory. The formulea to do so is extremely complex and I am expecting some software from Marc to help with these calculations. I was able to calculate the input rate as these formulae are relatively simple.

I have implemented (I believe) a two population model, with both excitatory and inhibitory populations. The model has a poisson generator for each excitatory neuron. These provides the initial stimulus for the excitatory population. The excitatory and inhibitory populations each have 0.1 * the population size, random connections to other neurons (self or cross population). Each population also has a spike detector attached.

Using the same variable values as previously used for the one population model, this yielded very little activity (17 ex-spikes and 0 in-spikes). However, if the weights are tweaked upwards, I found that the excitatory population started off barely spiking, until about 40ms where its rate increased exponetially to 50000 before 50ms, after which it maintained this firing rate. The inhibitory population displayed similar behaviour but on a smaller scale (as we would expect with a smaller population).

The resulting histograms derived from these spiking data clearly show a sigmoid shape. This indicates stable regular firing, though I have yet to confirm this.

Some of the examples have the following import:

from scipy.optimize import bisect

The scipy.optimize module does not appear to be available

pyNEST is a python extension and interface for the NEST Neural Simulation Tool. I will be using it to create my models for the NEST simulator. My first task is to get familiar with pyNEST and its features.

So far with pyNest, I have been trying out the basics; creating a single neuron (as opposed to a population of neurons), a Poisson Generator (a stochastic function) to interact with the neuron and a voltmeter to record the voltage of the neuron.

It would appear that the Poisson Generators can have several different rates which have corresponding convergence values. To begin with, I created a Poisson Generator which had just one rate. I set the initial rate to 40000.0 and a convergence value of 1.0. I then ran the simulator for one second (1000 ms) with a heuristic output. This lead to a neuron which never fired, with its membrane potential hovering around the -60mV point. Neurons need to reach aprox -55mV to fire and discharge to the rest potential rate of -70mV.

I next modified the convergence value to 2.0. This had much more satisfactory results. The neuron fired and discharged with a frequency of 45/second (45Hz). The peaks had a very uniformed structure. Increasing the convergence rate further causes the neuron to fire more frequently. Increasing it to 3.0 causes a spike frequency of 93Hz, more than double then before. A rate of 4.0 gives a frequency of 128Hz which suggests that the difference in growth is logarithmic.

I also tried altering the initial rate for the Poisson Generator. I decreased the rate from 40000 to 30000 in steps of 1000. At each step the result was less spikes within the same timeframe until 31000 where the neuron ceased to spike, between 37000 and 31000 the curves became increasingly less uniformed.

Increasing the Poisson rate increases the number of spikes. Interestingly the ratio is exactly the same as when I altered the convergence rate. Multiplying the convergence by 5 (2.0*5 = 10.0) produces 252 spikes. Doing the same to the Poisson rate (40,000 * 5 = 200,000) gives the same result.

At this point I decided to add an additional rate to the Poisson Generator. On doing so, the effects were immediately clear; a secondary rate can be used to amplify or to counteract the first by assigning positive or negative values for its convergence. I used two identical rates, with identical convergence values, the firing frequency was greatly increased. Setting the second convergence rate as the negative of the first (2.0, -2.0) causes the neuron to not fire at all. Setting the convergence rate at a lower negative value reduced the firing rate, providing the second Poisson rate is set significantly lower than the first. While I can see these effects, I can not establish exactly why we would do this.

1. What are the values being put into SetStatus() for the voltmeter and why are they required?
2. What is the final argument given in ConvergentConnect()? Only positive numbers, seems to offset the initial state of the neuron
3. Why would we use multiple rates in the Poisson Generator?
4. Is there any documentation for pyNEST?

This week was the start of the academic year proper. Still with that feverish bustle of freshers week, it was a hectic one. There is much to be done, people to see, places to get to. Something which takes just that bit longer now that the Roger Stevens Building is once again so overcrowded and congested. It is of course made worse by the fact that half the poor hapless freshers have no idea where they’re going. It has been quite comical seeing the familiar, bemused expression on their faces as they look across from lecture theatre 17 and wonder why they are now outside lecture theatre 20. I’m sure that’s exactly the effect the architect was going for.

Top of the agenda for myself and my fellow finalist Computing students was deciding our preferences for Final Year Projects. I know from bitter experience last year, that this is a daunting and often terrifying prospect. It certainly seemed to have hit a few people hard, it was getting to me even, despite the fact that I’ve been thinking about projects since before July. Even after the official deadline of 9am Friday, many were still stressing over what to decide.

As well as worrying about projects, we have already been given two pieces of coursework. The first being on the Tuesday, welcome to Third Year! Personally I do welcome this early coursework, despite the fact that one is an essay (I hate essays) as it does mean we can get some of it done now before our projects start, and before the torrent of other coursework that will undoubtedly follow in the next few weeks. I am taking 5 modules this semester, so the prospect of having 5 pieces of coursework and an FPY on the go does not sound fun to me.

I was appropriately reminded that for those lucky youngsters just starting their degrees, that this time of year is still quite literally a carnival. I went into the union yesterday to pick up some much needed stationary items, and was greeted by a ferris wheel and a burger stand parked outside the union. It’s all fun and games for some obviously, but not for me – its time to get some serious work done

I used to see foxes all the time when I lived in London, they were always rather interested in making a meal out of the cat. However it has taken three and a half years for me to come across a Leodien fox, last night being the first time ever.

They seem to be a lot more timid then there southern cousins. Whenever I encountered a fox in London it took a good soaking from the water gun or a barrage of rubber bands to convince them that the tasty cat-sized meal wasn’t worth the bother. I wasn’t even able to get a photo of the fox I saw last night because the moment I opened the window it was off like a shot. I am quite surprised by the difference in attitude. I always thought of foxes as being cunning and bold, but it seems they can also be complete pussies.

I miss Walthamstow and everything in it, and how it all was back in the day.

I miss my old flat; the dodgy peach wall paper, the dodgy cigarette smell, the tiny kitchen, the giant texaco sign in the hall way, the hole in the wall. I miss having the place to myself, I miss having all my buddies over, I miss being able to chose which. I miss my dad dropping by to get high with us.

I miss my job; I loved the people I worked with, some of whom I still talk to but others I will never see again. I miss the sense of purpose and belonging, I miss the responsibility, I miss having a wage, even a really shit one. I miss the Tryst on Friday evenings.

I miss the Garage; though it is still there it will never be the same again. I miss the freezing cold, the smell of dead chickens, the damp and mouldy sofas, the hot boxing.

I miss The Standard; I miss Supersonic, I miss watching bands like Route 215 and Cider, I miss the watered down Stella, the watered down steps outside the cab office. I miss the atmosphere, the people that I’ve met there. I miss getting so drunk I nearly fell into a coma.

I miss my friends; I miss Gaz (rest in peace mate). I miss Lee, Andre, Nathan and Claire. I miss Eddy, Damien and Sam. I miss Martin, Sarah, Al and Daryl. I miss Matt, Dave, Lewis and Saher. I miss Beccy. I miss Tessa. I miss Joe. When I think of all my best memories, you guys are all there. I miss going to the Dome and other dodgy clubs. I miss all the random parties, the random talks, the random nights down the pub and at the town hall.

I MISS MY OLD LIFE!

I know it does no good to look backwards, to dwell on the past. However, those who know me best might understand; the problems and issues that I am struggling with now didn’t start to manifest until I was about 21 and certainly didn’t become some troublesome that they interfere with my day to day life until recently. Since I came to uni, they have become so pronounced that I would give anything for them to go back to how they were before.

For that reason I can’t help but hanker for those good old days (even though I know they weren’t really that great) where everything seemed to work out, where I was able to relax enough to go out and enjoy myself properly. When I felt like I had everything and anything was possible and the stage was set for a bright future. Now I feel so jaded and dissolutioned that I look back at those days, shitty as they were, and feel that I’ll never have it that good again, that it’s all down hill from here and I have nothing to look forward to. I hate feeling this way, but its reality right now.