Regarding ML implementations of neuroscience findings. Assuming some sequential
input (e.g. 1D numeric/binary), has your team considered simply creating a
standard ANN (backprop trained if differentiable) but in which each neuron must
receive input in some predefined sequence (eg input X1 must
Numenta has published [a paper about applying sparsity to DL
systems](https://arxiv.org/abs/1903.11257). Outside of that however, not that I
know of.
Regarding the algorithm you've suggested, I don't understand how that would
work. Can you elaborate, please?
I guess what you're suggesting has
Note optionally: to increase the number of inputs/connectivity for each neuron
(e.g. A), each sequential input (e.g. X1; X2) could detect some static
combination of inputs (Numenta uses the terminology "pattern" here) from the
previous layer (e.g. x1-1, x1-2, x1-3, x1-4; x2-1 x2-2, x2-3, x2-4,
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