Reaction-Diffusion Systems for Information Processing: Why?

Filed under: Uncategorized — frm @ 9:50 pm

Several features of reaction-diffusion systems make them particularly promising candidates for computing by chemical reaction: They are capable of exhibiting varied and complex, ordered behaviour; different forms of r-d systems can sustain both stationary structures and information transmission over long distances with little loss, by means of excitation waves. Another useful property of certain r-d systems is their ability to cause some reagent to migrate towards areas where that chemical is already concentrated, making it possible for those systems to select for the highest among several peaks of concentration, which is crucial for the modelling of a Kohonen network, among other tasks.

One reason for the interest in chemical computation is simply the possibilities opened up by moving away from the digital computational paradigm in which almost all computer science to date has been conducted. Nobody knows where this might lead, but it is not unreasonable to suspect it might take us somewhere interesting. Another reason is the importance of chemical systems in the information processing performed by real brains and nerve cells.

The propagation of electrical signals within neurons is known to work by reaction-diffusion, and there is some reason to believe that substantial information-processing takes place on this level. In addition to this, the interaction between neurons in the brain and modulatory chemicals which spread by diffusion may be modelled by reaction-diffusion equations. It has become clear only relatively recently that these diffusing neuromodulators play a major role in brain function, and the details of this role are still being worked out. We have so far only seen the beginnings of work exploring the coupling between digital analogues of these chemicals and traditional neural networks.

Return of the Blog

Filed under: Uncategorized — frm @ 6:47 pm

I haven’t been keeping this blog up to date, partly because I wasn’t sure if anyone was reading it, but I’ve now decided to maintain it for my own benefit, regardless. All being well, I may even make daily updates on my progress.

RD SOFM example outputWhat I’ve invested the most time and effort into these last few months has been using a reaction-diffusion system to implement self-organising feature maps (Kohonen maps). This is working fairly well, and it’s a novel enough approach that I hope to be able to get a publishable paper out of it. I will post here about this soon, to summarise where I’m at so far and discuss progress as I go along.

Another project I’ve worked on is implementing ‘clustering’ with diffusion, on which topic I had a poster presentation accepted for a conference on Dynamics of Learning Behavior and Neuromodulation at the European Conference on Artificial Life 2007, although for complicated reasons, in the end I was not able to attend the conference myself.

As for this blog, I’ve just implemented a plugin to allow me to use LaTeX, in order to easily include equations. It’s called mimeTeX, and several different versions are available; I ended up with mimeTeX 1.1.2, which seems to work nicely except that you need to edit a line of the code to make it work – there’s a note about it on the page, which is fine except that when I copied and pasted the line in question it didn’t work, thanks to ’smart quotes’ – I needed to replace them with apostrophes by hand.