Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

@article{Buesing2011NeuralDA,
  title={Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons},
  author={Lars Buesing and J. Bill and Bernhard Nessler and W. Maass},
  journal={PLoS Computational Biology},
  year={2011},
  volume={7}
}
  • Lars Buesing, J. Bill, +1 author W. Maass
  • Published 2011
  • Computer Science, Medicine
  • PLoS Computational Biology
  • The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and… CONTINUE READING
    Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons
    95
    Oscillatory background activity implements a backbone for sampling-based computations in spiking neural networks
    Computing with noise in spiking neural networks
    Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks
    30
    Stochastic neural computation without noise
    3

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 82 REFERENCES
    Belief Propagation in Networks of Spiking Neurons
    51
    Probabilistic Computation in Spiking Populations
    37
    Bayesian Spiking Neurons I: Inference
    258
    Spatio-temporal correlations and visual signalling in a complete neuronal population
    1078
    Cortical Circuitry Implementing Graphical Models
    57
    The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs
    1500