Before revision:  We propose that background oscillations , an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for sampling-based computations in spiking neural networks. We first show that both in current-based and conductance-based neuron models, the level of background activity effectively defines the sampling temperature of the network. This mechanism allows brain networks to flexibly control the sampling behavior, either favoring convergence to local optima or promoting mixing. We then demonstrate that background oscillations can in this way structure stochastic computations into discrete sampling episodes. In each such episode, solutions are first explored at high temperatures before annealing to low temperatures favors convergence to a good solution .
After revision:  We propose that background oscillations , an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for sampling-based probabilistic inference, memory replay, multisensory cue combination and place cell flickering .