Unfortunately dendritic compartmentalization, spike timing etc are still not present. All efforts at models of SNNs that I know of have hit problems like riddled basins so far, that is what to look for to move past the limits of perceptron based networks IMHO.
As PAC learning with autograd and perceptrons is just compression, or set shattering, this paper is more of an optimization method that reduces ANN expressiveness through additional compression. Being able to control loss of precision is exciting though.
It may help in some cases, especially for practical use cases, but their unaddressed mention of potential problems with noisy loss functions needs to be addressed.
Human biological neurons can do XOR in the dendrites without hitting the soma at all is another example.
If you haven't heard about dendritic compartmentalization and plasticity, here is a paper.
https://www.cell.com/neuron/fulltext/S0896-6273(11)00993-7
> In conclusion our results support the view that experience can drive clustered synaptic enhancement onto neuronal dendritic subcompartments, providing fundamental architecture to circuit development and function
As PAC learning with autograd and perceptrons is just compression, or set shattering, this paper is more of an optimization method that reduces ANN expressiveness through additional compression. Being able to control loss of precision is exciting though.
It may help in some cases, especially for practical use cases, but their unaddressed mention of potential problems with noisy loss functions needs to be addressed.
Human biological neurons can do XOR in the dendrites without hitting the soma at all is another example.
If you haven't heard about dendritic compartmentalization and plasticity, here is a paper.
https://www.cell.com/neuron/fulltext/S0896-6273(11)00993-7
> In conclusion our results support the view that experience can drive clustered synaptic enhancement onto neuronal dendritic subcompartments, providing fundamental architecture to circuit development and function