Mission to build a simulated brain

In New Scientist a while ago there was an article entitled Mission to build a simulated brain begins which describes "An effort to create the first computer simulation of the entire human brain, right down to the molecular level...". It goes on to say "The hope is that the virtual brain will help shed light on some aspects of human cognition, such as perception, memory and perhaps even consciousness", and so on.

"...simulation of the entire human brain"?!!

1. That's rather a lot of neurons, and a mind-boggling number of connections. And that's not even going down to the molecular level. The state of the art is very limited compared to the goals of this project. I assume that the report is slightly inaccurate.
2. For instance, there are detailed computational models of how neurons function, and how they interact in small assemblies; there is the NEURON software for running these simulations. At a much higher level there are also general architectual models of whole regions of the brain.
3. In a few cases there are models that span several scales all the way from the bottom level to a fairly high level. I am thinking in particular of the visual cortex where we have the luxury of addressing the brain directly via the retina (which is effectively a layer of brain tissue). This has led to a very good (relative to other areas of the brain) neural model of visual processing in the brain.

"...and perhaps even consciousness"?!!

1. When I hear the "C" word I reach for my gun! People routinely use the "C" word to inject an air of mystery and importance into what they are saying. The fact is that it is a non-word that is used by people who haven't seriously reflected on what goes on inside their heads.
2. One book (amongst many) that I have found very insightful on this issue is The Artful Universe Expanded by John Barrow, in which he lucidly explains how evolution ensures that the universe imprints itself on the way that we we think. This means that the thing that we think with is not as free thinking as we would like to believe that it is. Is it at all surprising that we feel as if there is an inner light (call it "consciousness" if you want)?
3. Isn't it obvious that evolution ensures that you (whatever you are, human, fish, ant, or whatever) have a sense of "self" to encourage you to survive and reproduce, or have a sense of "host" to make you sacrifice yourself to save the nest? There will be "degrees" of "self" according to the "amount" of "processing" that is used to "implement" it. Yes, there are inverted commas everywhere because the terminology is not precisely defined.

What sort of brain studies should be given more prominence?

There is a lot of research going on near the bottom level, such as modelling neural chemical processes, modelling the dynamics of membrane potentials, modelling the interaction of small assemblies of neurons, etc. This is all very good reductionist work that potentially will lead to a deeper understanding.

However, I can't see much work going on that studies the theory of low-level brain-like processing, although there is a lot of neural modelling work. What do I mean by that? Brain-like processing is massively parallel, which needs its own special kind of "algorithm" that can be distributed across a large number of small interconnected processors. For really large parallel systems (e.g. brains) you can't go in and individually program every processor, but you can envisage the processors having inbuilt behaviours (i.e. a bootstrap program) that allow them to sort out their collective behaviour (i.e. self-organise) to do something macroscopically useful. We certainly need a lot more work on the theory of this type of massively parallel self-organising system.

There is a vast amount of research into what is called "neural networks", but which is nothing of the sort. As with the "C" word above, the "NN" phrase is used to guild otherwise rather mediocre research to make it seem more sophisticated than it is. A better phrase to describe most of this research is "non-linear adaptive filtering". The small subset of "NN" research that uses discrete firing events, rather than firing rates, is much better qualified to be called "NN".

There is the important distinction between supervised and unsupervised training:

1. Supervised training is usually used to guide an adaptive filter so that it approximates an externally specified output. From the point of view of brain-like processing this is not a very interesting type of adaptation.
2. Unsupervised training is much more interesting because it does not require an output to be externally specified, so the adaptive filter must discover for itself something "interesting" about its input; this is like answering an open-ended question, which is much more challenging than a question with a known answer. From the point of view of brain-like processing this is a very interesting type of adaptation.

Too much time is spent on supervised training by people doing what they think is true "NN" research. They are kidding themselves. How do they expect very large networks to be trained when all the external supervisor can do is to guide the network outputs? Is supervised training really going to help the deep inner workings of the network to learn anything useful? Wouldn't it be much better to distribute throughout the network the ability to discover "interesting" structure? Doesn't this sound rather like what unsupervised training is aiming to do anyway?

In Mission to build a simulated brain begins I want to see more theoretical work, especially on abstract brain-like processing and (more specifically) unsupervised training of neural networks that use discrete firing events, which would then guide the modelling work that they hope to do anyway. To understand what a large network simulation is doing you need a theory that is formulated at an appropriate level. It is not always a good idea to drill down to the bottom level of modelling (see my comments on reductionism versus emergentism).