ArticleFrontiers in computational neuroscience2015
Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.
Article in Frontiers in computational neuroscience, 2015. 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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7 citing papers in PubMed.
- Fractional-Order Spiking Bayesian Neural Model for Cognitive Computations.Neuroinformatics · 2026Article
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Bayesian encoding and decoding as distinct perspectives on neural coding.Nature neuroscience · 2023Review
- Cortical oscillations support sampling-based computations in spiking neural networks.PLoS computational biology · 2022Article
- Deterministic networks for probabilistic computing.Scientific reports · 2019Article
- Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.Frontiers in neuroscience · 2016Article
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7 authors.
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Abstract
The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.
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