ArticlePLoS computational biology2022
Cortical oscillations support sampling-based computations in spiking neural networks.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Ultrafast neural sampling with spiking nanolasers.Nature communications · 2025Article
- Modelling neural probabilistic computation using vector symbolic architectures.Cognitive neurodynamics · 2024Article
- Theta oscillations optimize a speed-precision trade-off in phase coding neurons.PLoS computational biology · 2024Article
- Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences.Scientific reports · 2024Article
- Rhythmic modulation of prediction errors: A top-down gating role for the beta-range in speech processing.PLoS computational biology · 2023Article
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Authors and funding
10 authors.
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Abstract
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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