ArticlePLoS computational biology2013
Stochastic computations in cortical microcircuit models.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Neural sampling from cognitive maps enables goal-directed imagination and planning.Nature machine intelligence · 2026Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Cortical oscillations support sampling-based computations in spiking neural networks.PLoS computational biology · 2022Article
- Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model.Frontiers in integrative neuroscience · 2022Article
- Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge.Proceedings of the National Academy of Sciences of the United States of America · 2021Review
- Deterministic networks for probabilistic computing.Scientific reports · 2019Article
- Evolutionary aspects of reservoir computing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.Neural computation · 2018Article
- The anchoring bias reflects rational use of cognitive resources.Psychonomic bulletin & review · 2018Review
- Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems.Frontiers in neuroscience · 2017Article
- Decoding subjective decisions from orbitofrontal cortex.Nature neuroscience · 2016Article
- Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.Frontiers in neuroscience · 2016Article
- Coordination Dynamics in Cognitive Neuroscience.Frontiers in neuroscience · 2016Review
- An event-based architecture for solving constraint satisfaction problems.Nature communications · 2015Article
- Event-driven contrastive divergence: neural sampling foundations.Frontiers in neuroscience · 2015Article
- Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition.PloS one · 2015Article
- NEVESIM: event-driven neural simulation framework with a Python interface.Frontiers in neuroinformatics · 2014Article
- Towards a "canonical" agranular cortical microcircuit.Frontiers in neuroanatomy · 2014Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Experimental data from neuroscience suggest that a substantial amount of knowledge is stored in the brain in the form of probability distributions over network states and trajectories of network states. We provide a theoretical foundation for this hypothesis by showing that even very detailed models for cortical microcircuits, with data-based diverse nonlinear neurons and synapses, have a stationary distribution of network states and trajectories of network states to which they converge exponentially fast from any initial state. We demonstrate that this convergence holds in spite of the non-reversibility of the stochastic dynamics of cortical microcircuits. We further show that, in the presence of background network oscillations, separate stationary distributions emerge for different phases of the oscillation, in accordance with experimentally reported phase-specific codes. We complement these theoretical results by computer simulations that investigate resulting computation times for typical probabilistic inference tasks on these internally stored distributions, such as marginalization or marginal maximum-a-posteriori estimation. Furthermore, we show that the inherent stochastic dynamics of generic cortical microcircuits enables them to quickly generate approximate solutions to difficult constraint satisfaction problems, where stored knowledge and current inputs jointly constrain possible solutions. This provides a powerful new computing paradigm for networks of spiking neurons, that also throws new light on how networks of neurons in the brain could carry out complex computational tasks such as prediction, imagination, memory recall and problem solving.
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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.