Manipulating spike count variance

Using a leaky integrate and fire neuron to generate spiking from fluctuating stimuli, but I wanted to generate data with different spike count variance. The problem came when two sources of variance worked to change other aspects of spike counts I didn't want. Increasing trial-by-trial noise promoted noise-driven activity, i.e., the rate went up, but the variance didn't increase that much. Increasing stimulus noise (which in truth shouldn't really be called noise, since it is these fluctuations that drive spiking), rapidly increased the firing rate and actually decreased spike variance.

These two forces needed to be balanced then. For future reference, muck about by decreasing stimulus noise, and increasing trial-by-trial noise.

Time-Correlated Gaussian Noise

Short Matlab script for generated time-correlated gaussian noise thanks to my supervisor. It takes the previous sample and generated a new sample with sampling rate dt based on a gaussian with time constant tau, mean mu and sd sig.

function y = tcorr_randn(y_prev,mu, sig, tau, dt)

y = mu + (y_prev - mu)*exp(-dt/tau) + sig*sqrt(1-exp(-2*dt/tau))*randn;

end

Interspike intervals: The fast way?

A reminder for myself more than anything

For a set of spike data distributed across time, i.e., a N x T matrix, where N is number of cells, T is total time steps dt:

function [ints, spt] = isi(spikes, dt)

% Uses spike data to output interspike intervals and accompanying spike times.

[N, T] = size(spikes);

ints = []; % intervals

spt = []; % spiketimes

% Find isi for each cell

for n = 1:N

    spiketime = find(spikes(n,:) == 1);

    ints = [ints,spiketime(1),diff(spiketime)];

    spt = [spt,spiketime];

end

if isempty(ints)

    ints = inf;

end

ints = ints/(dt*T);

[spt col] = sort(spt, 'ascend'); spiketimes in ascending order

ints = ints(col); intervals sorted by spiketime

end

I would very much like to know if there is a faster way, particularly if it means I could find for all cells at once. Currently it isn't completely matlab efficient.

Reading papers

I've notices recently that the focus I have when reading articles has changed quite dramatically. In undergraduate, I really didn't care how a researcher did anything or how they validated their claims, I just wanted to see the interpretation of their results so I could paraphrase them and be done with it. Now I'll spend days pouring over their method and results section to see if there is absolutely anything I dislike. I'll spend time trying to simulate some small part of it to get some understanding, make notes on anything that seemed absurdly difficult or just redundant.

 I do tend to notice now though that the moments of genius people get are always in these sections, although the language has to be so plain that you really can't express the joy you get from making some realisation that made your method work. It seems a shame to paraphrase this now.

Computational Neuroscience

My interest in Computational Neuroscience and AI started in some of the dissatisfaction I had with Psychology while studying for my BSc. I felt that a lot of the time there wasn't any coherent theory to actually understand the results of the experiments being done and it often seemed like an exercise in collecting more data. Sometimes there would be a 'theory' proposed, but this was just a qualitative expression that could often be twisted to fit the data. The formalism introduced in Computational Neuroscience and AI gave some concrete predictions that could actually be evaluated.

However there have been times while doing my MSc that I feel I'm just playing with properties of matrices, and that there are people out there better suited for doing it, i.e., physicists, mathematicians, etc. Perhaps I'm just making excuses.

Musings on Alien Races

I've noticed in Sci-fi that Alien races are often depicted as being completely homogeneous. Same religion, same language, same dress sense, same ideals. This is true unless it is a specific plot point, and even then they are only limited to two warring factions at best.

One notable distinction to this is the Covenant in the 'Halo' series. Admittedly this is an amalgamation of different species held together by a unifying religion, but off the top of my head this is one example where an Alien 'race' isn't treated as homogeneous. There are several belief systems, several cultures, etc. In fact, another game seems to make fun of this sci-fi trope, i.e., in 'Mass Effect 2' where Mordin notes that the human race are good test subjects due to their relative genetic diversity, hence explaining why all other races are so homogeneous.

So, assuming that alien races are not so homogeneous, I wonder whether the reason we have yet to be invaded by aliens is that they have yet to hold a referendum on invasion yet.

Information Theory

Recently I've started watching David McKay's lecture series on Information Theory on videolectures.net (http://videolectures.net/course_information_theory_pattern_recognition/). Around September when choosing courses to take for the upcoming semester I had very little idea of what Information Theory actually was and since there were other more obvious courses to take, it barely registered a second thought. Nevertheless having had some facet pop up in every course I had taken that semester, even if it was just a passing mention to Shannon entropy, I thought perhaps I had missed something very worthwhile.

I've just finished lecture 4, and so far McKay has taken me through a tour of Shannon's source coding theorem and he has convinced of what a marvel it really is. I don't really have time to do all the surrounding reading (although I did read the chapter on Information Theory in Theoretical Neuroscience), but it has been excellent in giving an intuition for how the basis of the field was constructed. The exercises he takes the class through connect neatly together when you start to think about them and it becomes really satisfying when you start to know what is coming next, which happens more often than I was expecting.

Even though my MSc thesis topic appears to have changed (I'll now be looking at developing a Wiener kernel to predict variability in spike trains), it still looks as if this will be useful in understanding neural encoding. So I'll stick with it for now.