This is an interesting paper:
by Patrick Crotty, Daniel Schult, Ken Segall (Colgate University)
“With the goal of understanding the intricate behavior and dynamics of collections of neurons, we present superconducting circuits containing Josephson junctions that model biologically realistic neurons. These “Josephson junction neurons” reproduce many characteristic behaviors of biological neurons such as action potentials, refractory periods, and firing thresholds. They can be coupled together in ways that mimic electrical and chemical synapses. Using existing fabrication technologies, large interconnected networks of Josephson junction neurons would operate fully in parallel. They would be orders of magnitude faster than both traditional computer simulations and biological neural networks. Josephson junction neurons provide a new tool for exploring long-term large-scale dynamics for networks of neurons.”
Advantages of using RSFQ-style architectures include the non-linear response of the elements and the analogue processing capability which means that you can mimic more ‘logical’ neurons with fewer ‘physical’ elements. I’m pretty sure that this is true. In addition, you can think of other wonderful ideas such as using SQUIDs instead of single junctions (hmm, I wonder where this train of thought might lead) and then apply non-local (or global) magnetic fields to adjust the properties of the neural net. Which might be a bit like adjusting the global values of a particular neurotransmitter.
I’m a bit worried about this approach though. Current superconducting technologies tend to have a low number of wiring layers (<5), and as such are pretty much a 2 dimensional, planar technology. The maximum tiling connectivity you can get from a single layer planar architecture is presumably 6 nearest neighbour unit cell. (Hexagonal close packing). The three dimensional packing in a real brain gives you a higher intrinsic level of connectivity, even though the structure of the neocortex is only quasi-3-dimensional (it is more like 2D sheets crumpled up, but even these '2D' sheets have a fair amount of 3D connectivity when you look closely. In a real brain, each neuron can have tens of thousands of differently weighted inputs (the fan-in problem). Try building that into your mostly-planar circuit 🙂
One good thing about using analogue methods is that not all the neurons need to be identical. In fact having a parameter spread in this massively parallel architecture probably doesn't hurt you at all (it might even help). Which is good, as current Josephson junction foundries have issues with parameter spreads in the resulting digital circuitry (they are nowhere near as closely controlled as semiconductor foundries).
The paper claims that the tens of thousands of neurons in a neocortical column might be simulable using this method. I think that with present LSI JJ technology this is very optimistic personally… but even considering the connectivity, parameter spreading and fan-in problems, I think this is a very interesting area to investigate experimentally.
I’ve actually written a bit about this topic before:
In that blogpost there were some links to experiments performed on simple Josephson junction neuron circuits in the 1990’s.