Cortical oscillations implement a backbone for sampling-based computation in spiking neural networks
This addresses a fundamental challenge in neural computation for understanding brain function, though it is incremental in connecting existing concepts.
The paper tackles the mixing problem in spiking neural networks, where switching between dissimilar valid states is difficult, by proposing that cortical oscillations act as an effective temperature to modulate exploration, linking them to sampling-based computation and phenomena like memory replay.
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.