Various data suggest that the brain carries out probabilistic inference. Models that perform inference through sampling are particularly appealing since instead of requiring networks to perform sophisticated mathematical operations, they can simply exploit stochasticity in neuron behavior. However, sampling from complex distributions is a hard problem. In particular, mixing behavior is often very sensitive to the temperature parameter which controls the stochasticity of the sampler . 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 .
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately. Often, this requires visiting multiple interpretations of the available information or multiple solutions to an encountered problem. This gives rise to the so-called mixing problem: since all of these "valid" states represent powerful attractors, but between themselves can be very dissimilar, switching between such states can be difficult . We propose that cortical oscillations can be effectively used to overcome this challenge. By acting as an effective temperature, background spiking activity modulates exploration. Rhythmic changes induced by cortical oscillations can then be interpreted as a form of simulated tempering. We provide a rigorous mathematical discussion of this link and study some of its phenomenological implications in computer simulations. This identifies a new computational role of cortical oscillations and connects them to various phenomena in the brain, such as sampling-based probabilistic inference, memory replay, multisensory cue combination and place cell flickering .
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Various data suggest that the brain carries out probabilistic inference. Models that perform inference through sampling are particularly appealing since instead of requiring networks to perform sophisticated mathematical operations, they can simply exploit stochasticity in neuron behavior. However, sampling from complex distributions is a hard problem. In particular, mixing behavior is often very sensitive to the temperature parameter which controls the stochasticity of the sampler
Being permanently confronted with an uncertain world, brains have faced evolutionary pressure to represent this uncertainty in order to respond appropriately. Often, this requires visiting multiple interpretations of the available information or multiple solutions to an encountered problem. This gives rise to the so-called mixing problem: since all of these "valid" states represent powerful attractors, but between themselves can be very dissimilar, switching between such states can be difficult
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background oscillations , an ubiquitous phenomenon throughout the brain, can mitigate this issue and thus implement the backbone for
cortical oscillations can be effectively used to overcome this challenge. By acting as an effective temperature, background spiking activity modulates exploration. Rhythmic changes induced by cortical oscillations can then be interpreted as a form of simulated tempering. We provide a rigorous mathematical discussion of this link and study some of its phenomenological implications in computer simulations. This identifies a new computational role of cortical oscillations and connects them to various phenomena in the brain, such as
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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
probabilistic inference, memory replay, multisensory cue combination and place cell flickering