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Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

https://doi.org/10.1101/696088
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Differences in Gamma Frequencies across Visual Cortex Restrict Their Possible Use in Computation
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State Dependence of Noise Correlations in Macaque Primary Visual Cortex
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Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics
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Perception as Bayesian Inference
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Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties
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Efficient computation and cue integration with noisy population codes
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Neuronal synchrony does not correlate with motion coherence in cortical area MT
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Inhibition dominates sensory responses in the awake cortex
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Context-dependent computation by recurrent dynamics in prefrontal cortex
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Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities
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What does gamma coherence tell us about inter-regional neural communication?
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Flexible gating of contextual influences in natural vision
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Synchrony and covariation of firing rates in the primary visual cortex during contour grouping
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Bayesian inference with probabilistic population codes
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Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
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Stimulus-dependent variability and noise correlations in cortical MT neurons
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
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Stabilized supralinear network can give rise to bistable, oscillatory, and persistent activity
inferredcites →https://doi.org/10.1073/pnas.1816766116openalex-snapshot
Stimulus complexity shapes response correlations in primary visual cortex
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Theory of orientation tuning in visual cortex.
inferredcites →https://doi.org/10.1093/cercor/bhu091openalex-snapshot
Stimulus Dependence of Gamma Oscillations in Human Visual Cortex
inferredcites →https://doi.org/10.1109/5.58337openalex-snapshot
Backpropagation through time: what it does and how to do it
inferredcites →https://doi.org/10.1126/science.1179867openalex-snapshot
Decorrelated Neuronal Firing in Cortical Microcircuits
inferredcites →https://doi.org/10.1126/science.1195870openalex-snapshot
Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment
inferredcites →https://doi.org/10.1126/science.275.5306.1593openalex-snapshot
A Neural Substrate of Prediction and Reward
inferredcites →https://doi.org/10.1146/annurev-neuro-062111-150444openalex-snapshot
Mechanisms of Gamma Oscillations
inferredcites →https://doi.org/10.1162/089976698300017214openalex-snapshot
Chaotic Balanced State in a Model of Cortical Circuits
inferredcites →https://doi.org/10.1162/neco_a_00472openalex-snapshot
Analysis of the Stabilized Supralinear Network
inferredcites →https://doi.org/10.1167/9.4.19openalex-snapshot
Perceptual organization in the tilt illusion
inferredcites →https://doi.org/10.1167/9.8.19openalex-snapshot
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Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons
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Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model
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Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability
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Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Advances in the computational understanding of mental illness
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Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
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Synapse-type-specific competitive Hebbian learning forms functional recurrent networks
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Predictive Coding in Cortical and Corticothalamic Hierarchical Networks
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Task-induced neural covariability as a signature of approximate Bayesian learning and inference
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Predictive coding of natural images by V1 activity revealed by self-supervised deep neural networks
inferred← citeshttps://doi.org/10.1101/2020.11.18.389197openalex-snapshot
Recurrent circuit based neural population codes for stimulus representation and inference
inferred← citeshttps://doi.org/10.1101/2025.07.03.663069openalex-snapshot
Natural gradient Bayesian sampling automatically emerges in canonical cortical circuits
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Canonical cortical circuits: A unified sampling machine for static and dynamic inference
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Reduction in social learning and increased policy uncertainty about harmful intent is associated with pre-existing paranoid beliefs: Evidence from modelling a modified serial dictator game
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Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks
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Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability
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Cognitive effort, efficient coding and non-invasive fMRI measurement of their relation in sensorimotor responses
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