Edit -- I'm an idiot and misread OP. I read "inspired by nature", rather than specifically "biology". Keeping everything below here just for goofs. I think the only thing that applies is the Computational Beauty of Nature book recc.
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There's a bunch of methods that come out of casting an optimization or search problem into a thermal system with an artificial energy and corresponding conjugate (artificial) temperature.
Most people are familiar with (Gibbs-ish) Monte Carlo methods, for example. Then there's simulated annealing, tempering, parallel tempering, etc. Simulated annealing is universal optimizer with remarkable richness coming from the "energy landscape" perspective. Recent work by Riccardo zecchina has studied neural nets from this perspective, and the whole (largely) Italian gang of glassy landscape people have done really cool stuff with it including fundamental CS stuff like K-SAT.
Lots of optimization algos use tricks that exploit analogies to thermal systems, given a sufficiently clever "Hamiltonion".
I'm not up to date with the latest and greatest in NN training stuff, but I know a few of the preferred optimization algorithms used some form of gradient descent often with some artificial "momentum". Can't remember what it was called, maybe Adam or something? I know Michael Jordan at Berkeley does a lot cool stuff with along these lines.
Very rambly message I see now, so I'll stop and just give some references/recs and maybe polish up later.
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There's a bunch of methods that come out of casting an optimization or search problem into a thermal system with an artificial energy and corresponding conjugate (artificial) temperature.
Most people are familiar with (Gibbs-ish) Monte Carlo methods, for example. Then there's simulated annealing, tempering, parallel tempering, etc. Simulated annealing is universal optimizer with remarkable richness coming from the "energy landscape" perspective. Recent work by Riccardo zecchina has studied neural nets from this perspective, and the whole (largely) Italian gang of glassy landscape people have done really cool stuff with it including fundamental CS stuff like K-SAT.
Lots of optimization algos use tricks that exploit analogies to thermal systems, given a sufficiently clever "Hamiltonion".
I'm not up to date with the latest and greatest in NN training stuff, but I know a few of the preferred optimization algorithms used some form of gradient descent often with some artificial "momentum". Can't remember what it was called, maybe Adam or something? I know Michael Jordan at Berkeley does a lot cool stuff with along these lines.
Very rambly message I see now, so I'll stop and just give some references/recs and maybe polish up later.
A few recommendations--
Nature of computation - Cris Moore I think
Information and physics(?) - Mezard and montanari
Computational beauty of nature -Flack(?)
Newish book from SFI from a conference I was at a few years ago may interest you, The Energetics of Computing in Life and Machines https://www.amazon.com/dp/1947864076/ref=cm_sw_r_apan_i_JQXY...
And most emphatically, Information, inference, and learning - Mackay