'Noise in Cognition: Bug or Feature?' https://psyarxiv.com/438nd/
"First, both perceptual and preferential judgments show that sensory and motor noise may only play minor roles, with most noise arising in the cognitive computations."
"...we propose that the brain approximates probabilistic inference with a local sampling algorithm, one that uses randomness to drive its exploration of alternative hypotheses."
Sort of obvious? But good to see it out there.
Which mechanisms for neural exploration/sampling do you think of as most likely?
1. Periodic and quasi-periodic sweeps
2. 'Real' random perturbations
3. Pseudo-random and/or chaotic pattern generators
4. Deterministic, non-periodic 'principled' exploration
I suspect that the answer is 'all of the above'.
Am I missing any plausible mechanisms?
@DrYohanJohn 1 and 3 in combined approach. 2 is undoable and 4 is too costly.
I think quantum effects at the level of vesicle release can lead to 'real' random fluctuations, so fine balance between excitation and inhibition could be used to 'amplify' synaptic noise. So #2 could be doable.
@DrYohanJohn @vickerse That's an interesting idea. I'm a synaptic physiologist, and I've always wondered about the fact that the random release probability of vesicles is much higher at inhibitory synapses compared to excitatory synapses. Your comment makes me wonder if that's related to keeping the 'balance'? Certainly, there are less inhibitory synapses than excitatory ones. What are the predictions, if there is a change in that balance?
@hkl @DrYohanJohn i wonder if anyone has ever measured coherence of spontaneous release across local mixed networks of inhibitory and excitatory neurons....or perhaps "state" driven changes in mean spontaneous release rate that could coordinate short term plasticity-driven modulations of release probability.
@vickerse @DrYohanJohn
There was a recent paper that showed that the frequency of mEPSCs and mIPSCs are regulated differently across the circadian cycle.
https://doi.org/10.1016/j.neuron.2019.11.011
@DrYohanJohn @vickerse Thanks! I'm not well-versed in the dynamic network field, so it's hard to grasp how increased excitation could lead to hyseresis. If you have recommendations on a review article that explains this in simple terms, I'd appreciate it 
Not sure if there is a paper specifically in these terms. I did a tutorial on competitive networks with a Python implementation here:
Note that this is at a much more coarse-grained level than spiking and PSPs, but similar phenomena are easy to implement with spiking neurons.
