ArticleAdvances in neural information processing systems2022
Natural gradient enables fast sampling in spiking neural networks.
Article in Advances in neural information processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Combining sampling and attractor dynamics in spiking models of head direction systems.PLoS computational biology · 2026Article
- Brain-like variational inference.ArXiv · 2025Article
- Modelling neural probabilistic computation using vector symbolic architectures.Cognitive neurodynamics · 2024Article
- Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.bioRxiv : the preprint server for biology · 2023Article
- Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb.Advances in neural information processing systems · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
For animals to navigate an uncertain world, their brains need to estimate uncertainty at the timescales of sensations and actions. Sampling-based algorithms afford a theoretically-grounded framework for probabilistic inference in neural circuits, but it remains unknown how one can implement fast sampling algorithms in biologically-plausible spiking networks. Here, we propose to leverage the population geometry, controlled by the neural code and the neural dynamics, to implement fast samplers in spiking neural networks. We first show that two classes of spiking samplers-efficient balanced spiking networks that simulate Langevin sampling, and networks with probabilistic spike rules that implement Metropolis-Hastings sampling-can be unified within a common framework. We then show that careful choice of population geometry, corresponding to the natural space of parameters, enables rapid inference of parameters drawn from strongly-correlated high-dimensional distributions in both networks. Our results suggest design principles for algorithms for sampling-based probabilistic inference in spiking neural networks, yielding potential inspiration for neuromorphic computing and testable predictions for neurobiology.
Identifiers
37476623PMC10358281W4281702935What OpenQuestion holds
Registered trials
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