[orbifold]

I agree with these points, however the main advantage of the method presented in the paper is precisely that both the forward propagation and backward propagation can be seen as being performed by a network operating on temporally sparse events. We absolutely had event-based sensors and control as a motivation in mind. The fact that you can write down the connectivity of the neurons in terms of a weight matrix, does not mean that it can't be sparse. Since you are actually processing one spike at a time (potentially asynchronously), you don't need to implement any matrix multiplication. Current neuromorphic hardware achieves at least some degree of sparsity in their synaptic crossbars (BrainScales2, Spinnaker) or largely eliminates them like Loihi.

[FrereKhan]

Ultra-low-power neuromorphic processors such as DynapSE[1] have been cross-bar free for several years now, making them a perfect fit for sparse networks (both weight- and activity-sparsity). [1] https://arxiv.org/abs/1708.04198

[orbifold]

Yes, that would be another example.

[periheli0n]

Yes, the algorithm you proposed is impressive and has the potential to become a game-changer.

However, I think the MNIST and the Ying/Yang dataset, using latency-coding, are not the ideal example to demonstrate its performance.

These datasets are useful to demonstrate nonlinear classification, and it's certainly great to see that the spiking network performs competitively. However, the transformation into a latency code costs time, in terms of computation, and also in terms of representation, before even one item is classified. Perceptron-based ANNs with continuous outputs don't require this step and will always have an edge over spiking networks in such scenarios.

I think what the field is really lacking is an ML problem that can leverage spiking networks directly, that does not require costly conversion of data into a representation that is suitable for spiking networks.

[orbifold]

I agree that the choice of task is not ideal. It is something that I struggled quite a bit with, since coming up with a good task can be a lot of work. Unfortunately even some of the "neuromorphic" datasets that are in use can be solved by massive temporal averaging or result in reduced performance of the network relative to "analog" temporal input (e.g. on Google Speech Commands). I'm collaborating with a group that is interested in event-based vision and control, so hopefully this will result in more practical/impressive demonstrations in the future.

[PaulHoule]

I have always wondered if results against the MNIST digits are generic. One might think it would work if you put in some other digits such as 一, 二, 三, 四 would they cluster the same with tSNE?