Does the brain actively recall memories by lowering temperature (thus increasing free energy and lowering the activation-threshold for ordered phase-transition), or by *increasing* temperature (thus lowering the relative-activation-threshold via stochastic resonance), or both?
Assume memory m is an ordered state in recurrent cell assembly M.
M is usually in any of its many disordered states due to stochastic activity in its neighbourhood, but there's always a threshold for *pattern-similarity* to m (wrt phase/frequency of constituent neurons) above which its internal feedback loops suffice to autocomplete the rest of it.
Here are ways you can lower that threshold and likelify M's phase-transition into m →
A) STOCHASTIC RESONANCE
Assume that the transition to m depends on a threshold for total activation of m *relative to* competitive activity nearby.
Now, to recall m, simply add uniform noise to the general neighborhood of M. The noise is merely additive for neurons not in M, whereas it's *multiplicative* for M due to its loops. The noise relatively amplifies activity in recurrent networks, but only if they survive the noise (ie exceed the "error threshold" for mutation).
B) FREE UP ENERGY FOR ORDER
Assume that the height of the threshold depends on the stability of sub-completions of m, because the pattern can't complete if its progress gets partially reset all the time.
Now, you can make it easier to recall m by lowering neighborhood noise. But if there are competing memories in that same neighborhood, you may end up accidentally stabilizing the wrong one.
I actually think the brain uses both.
I think it's usually model B. I think effortfwl recall correlates w alpha oscillations bombarded across the cortex, and this reduces temperature in non-bombarded areas. Successfwl recall correlates w P300 EEG-signature, which according to Dehaene indicates a preponderance of inhibitory neurons.
But I also think I've had personal success w "deconcentration of concentration" to recall blocked Tip-of-the-Tongue memories. And this corresponds to model A!
