ArticleScientific reports2019
Deterministic networks for probabilistic computing.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Role of fast-spiking interneurons in modulating across-trial variability and within-trial correlations in the striatum.PLoS computational biology · 2026Article
- Conductance-based dendrites perform Bayes-optimal cue integration.PLoS computational biology · 2024Article
- Chaotic neural dynamics facilitate probabilistic computations through sampling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Coherent noise enables probabilistic sequence replay in spiking neuronal networks.PLoS computational biology · 2023Article
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- An overview of brain-like computing: Architecture, applications, and future trends.Frontiers in neurorobotics · 2022Review
- Article
- Emerging Artificial Neuron Devices for Probabilistic Computing.Frontiers in neuroscience · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuronal network models of high-level brain functions such as memory recall and reasoning often rely on the presence of some form of noise. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. In vivo, synaptic background input has been suggested to serve as the main source of noise in biological neuronal networks. However, the finiteness of the number of such noise sources constitutes a challenge to this idea. Here, we show that shared-noise correlations resulting from a finite number of independent noise sources can substantially impair the performance of stochastic network models. We demonstrate that this problem is naturally overcome by replacing the ensemble of independent noise sources by a deterministic recurrent neuronal network. By virtue of inhibitory feedback, such networks can generate small residual spatial correlations in their activity which, counter to intuition, suppress the detrimental effect of shared input. We exploit this mechanism to show that a single recurrent network of a few hundred neurons can serve as a natural noise source for a large ensemble of functional networks performing probabilistic computations, each comprising thousands of units.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.