Following on from preceding discussion, Doru kindly provided this very interesting link to information about a new chip designed at MIT which is designed to mimic the function of real neurons.
I hadn’t realised how much was going on, but it seems MIT is by no means alone in wanting to create such a chip. In the previous post I mentioned Dharmendra Modha’s somewhat controversial simulations of mammal brains: under his project leadership IBM, with DARPA participation, is now also working on a chip that simulates neuronal interaction. But while MIT and IBM slug it out those pesky Europeans had already produced a neural chip as part of the FACETS project back in 2009. Or had they? FACETS is now closed and its work continues within the BrainScaleS project working closely with Henry Markram’s Blue Brain project at EPFL, in which IBM, unless I’m getting confused by now, is also involved. Stanford, and no doubt others I’ve missed, are involved in the same kind of research.
So it seems that a lot of people think a neuron-simulating chip is a promising line to follow; if I were cynical I would also glean from the publicity that producing one that actually does useful stuff is not as easy as producing a design or a prototype; nevertheless it seems clear that this is an idea with legs.
What are these chips actually meant to do? There is a spectrum here from the pure simulation of what real brains really do to a loose importation of a functional idea which might be useful in computation regardless of biological realism. One obstacle for chip designers is that not all neurons are the same. If you are at the realist end of the spectrum, this is a serious issue but not necessarily an insoluble one. If we had to simulate the specific details of every single neuron in a brain the task would become insanely large: but it is probable that neurons are to some degree standardised. Categorising them is, so far as I know, a task which has not been completed for any complex brain: for Caenorhabditis elegans, the only organism whose connectome is fully known, it turned out that the number of categories was only slightly lower than the number of neurons, once allowance was made for bilateral symmetry; but that probably just reflects the very small number of neurons possessed by Caenorhabditis (about 300) and it is highly likely that in a human brain the ratio would be much more favourable. We might not have to simulate more than a few hundred different kinds of standard neuron to get a pretty good working approximation of the real thing.
But of course we don’t necessarily care that much about biological realism. Simulating all the different types of neurons might be a task like simulating real feathers, with the minute intricate barbicel latching structures – still unreplicated by human technology so far as I know – which make them such sophisticated air controllers, whereas to achieve flight it turns out we don’t need to consider any structure below the level of wing. It may well be that one kind of simulated neuron will be more than enough for many revolutionary projects, and perhaps even for some form of consciousness.
It’s very interesting to see that the MIT chip is described as working in a non-digital, analog way (Does anyone now remember the era when no-one knew whether digital or analog computers were going to be the wave of the future?). Stanford’s Neurogrid project is also said to use analog methods, while BrainScaleS speaks of non-Von Neumann approaches, which could refer to localised data storage or to parallelism but often just means ‘unconventional’. This all sounds like a tacit concession to those who have argued that the human mind was in some important respects non-computational: Penrose for mathematical insight, Searle for subjective experience, to name but two. My guess is that Penrose would be open-minded about the capacities of a non-computational neuron chip, but that Searle would probably say it was still the wrong kind of stuff to support consciousness.
In one respect the emergence of chips that mimic neurons is highly encouraging: it represents a nearly-complete bridge between neurology at one end and AI at the other. In both fields people have spoken of ‘connectionism’ in slightly different senses, but now there is a real prospect of the two converging. This is remarkable – I can’t think of another case where two different fields have tunnelled towards each other and met so neatly – and in its way seems to be a significant step towards the reunification of the physical and the mental. But let’s wait and see if the chips live up to the promise.