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Wow super interesting. I'm assuming this would be a fixed network thought? Could you adapt this hardware to change the weights and have the network learn (rather than passively interpret input data?)

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We can do dynamic networking — the routing tables for the hardware is reloadable at runtime — but it's sufficiently difficult to do the routing computations that we only do that when the simulation is stopped at the moment. (We actually usually use the machine to compute its own routing tables; that's the fastest approach since it is a massively parallel problem.)

However, the effective network can route dynamically (by faking things on top of an initially-zero-weight all-to-all connection pattern between two neuron populations). One of our PhD students is working on this, and on the types of dynamic online learning that this enables, modelling the dynamic generation and removal of synapses that occurs in biological neurons. We also support tuning of connection weights in response to the history of synaptic activity via Spike Timing Dependent Plasticity (STDP), and have done for a few years (using earlier generations of the hardware config).

FWIW, this is based on SpiNNaker, which is a system with a million CPU cores and a custom low-power multicast network backplane. It's possible to do simpler neural models with much less power than we do (and some of our competitors do just that) but it's not at all clear that those simple models are actually sufficiently biologically relevant to produce enough of the phenomena that we care about. Having a system flexible enough to support a dynamic research agenda is vital, but does increase the energy cost per neuron and per synapse.

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