Belief Propagation in Networks of Spiking Neurons

DOI10.1162/NECO.2009.08-08-837zbMath1171.92014OpenAlexW1963660541WikidataQ48557346 ScholiaQ48557346MaRDI QIDQ3182480

Rodney J. Douglas, Andreas Steimer, Wolfgang Maass

Publication date: 9 October 2009

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2009.08-08-837

Mathematics Subject Classification ID

Foundations and philosophical topics in statistics (62A01) Graph theory (including graph drawing) in computer science (68R10) Neural biology (92C20) Psychophysics and psychophysiology; perception (91E30)

Related Items (9)

A family of CCCP algorithms which minimize the TRW free energy ⋮ Spike-Based Probabilistic Inference in Analog Graphical Models Using Interspike-Interval Coding ⋮ Neural network implementation of inference on binary Markov random fields with probability coding ⋮ Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation ⋮ Active Inference, Belief Propagation, and the Bethe Approximation ⋮ Computing with Spikes: The Advantage of Fine-Grained Timing ⋮ Reward-Modulated Hebbian Learning of Decision Making ⋮ A Systematic Method for Configuring VLSI Networks of Spiking Neurons ⋮ Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference

Cites Work

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• Bayesian Spiking Neurons I: Inference
• Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms
• Training Recurrent Networks by Evolino
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• Factor graphs and the sum-product algorithm
• Spiking Neuron Models
• Bayesian Computation in Recurrent Neural Circuits
• Neural networks and physical systems with emergent collective computational abilities.

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