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This paper imports an arbitrarily-chosen aspect of cortical architecture — topological maps of function — and ignores every other aspect of biological neural tissue. The resulting …

I agree. The use case needs to justify having state, otherwise the ideal architecture is something like NullHop. Temporal signal processing / vision processing tasks are ideal for …

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-…

If you bring activation sparsity into the mix, the advantage of SNN processors over GPUs/TPUs becomes more clear. Loss-gradient-based optimisation approaches are great because they…

It's not quite correct to say this is only for achieving deep learning. Gradient-based parameter optimisation is still a useful tool, even for small shallow networks that would be …

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