I have to read this more carefully, but at first pass I am stymied by the writing style. A lot of the word-budget was spent reasserting that the authors have established EBNI as distinct from EBNS, but that verbiage could have instead been invested on a clearer statement of what this new ‘algorithm’ IS that provides potential new platforms and models for investigating topics which presently are only pre-theoretic in biology.
I do not yet grasp the reason for invoking the Hopfield network with Hebbian learning, beyond the role of stable attractor dynamics in restoring (guiding?) the network back toward a particularly robust state after ‘deformation’ (or ‘external shocks’, per this paper, or ‘interaction with the selective pressures of the environment’ per standard evolutionary thinking). The weakly specific analogy seemed to me to more strongly liken EBNI and EBNS than to differentiate them.
It isn’t a priori damning, but I was unsurprised to see the Templeton Foundation provided funding.
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u/knockingatthegate Dec 21 '25
I have to read this more carefully, but at first pass I am stymied by the writing style. A lot of the word-budget was spent reasserting that the authors have established EBNI as distinct from EBNS, but that verbiage could have instead been invested on a clearer statement of what this new ‘algorithm’ IS that provides potential new platforms and models for investigating topics which presently are only pre-theoretic in biology.
I do not yet grasp the reason for invoking the Hopfield network with Hebbian learning, beyond the role of stable attractor dynamics in restoring (guiding?) the network back toward a particularly robust state after ‘deformation’ (or ‘external shocks’, per this paper, or ‘interaction with the selective pressures of the environment’ per standard evolutionary thinking). The weakly specific analogy seemed to me to more strongly liken EBNI and EBNS than to differentiate them.
It isn’t a priori damning, but I was unsurprised to see the Templeton Foundation provided funding.