[venachescu]

I'm pretty surprised by all the focus on spiking neural networks too; they are really still much more of an academic topic and I have never heard of any real practical applications (yet).

There are a number of methods of training these kinds of networks like Spike Timing Dependent Plasticity (STDP) which is essentially a reinforcement learning algorithm that increases the weights between neurons that spike often together and modulates the increases by a reward signal. However, all these methods are really more focused on replicating and modeling the biological phenomena and not being performant.

In theory, spiking networks should be much more efficient, real neurons are able to take advantage of many non-linear effects to create incredibly complex analog to digital (spikes) computation in every cell using incredibly little energy - our brains only use about 20 watts of power.

But, the current approach to CPUs and even GPUs means simulating all this stuff is ridiculously inefficient and ultimately looks nothing like the real thing.