Corpus ID: 14240795

Hamiltonian synaptic sampling in a model for reward-gated network plasticity

@article{Yu2016HamiltonianSS,
  title={Hamiltonian synaptic sampling in a model for reward-gated network plasticity},
  author={Zhaofei Yu and David Kappel and R. Legenstein and S. Song and F. Chen and W. Maass},
  journal={ArXiv},
  year={2016},
  volume={abs/1606.00157}
}

Zhaofei Yu, David Kappel, +3 authors W. Maass

Published 2016

Computer Science, Biology

ArXiv

Experimental data show that synaptic connections are subject to stochastic processes, and that neural codes drift on larger time scales. These data suggest to consider besides maximum likelihood learning also sampling models for network plasticity (synaptic sampling), where the current network connectivity and parameter values are viewed as a sample from a Markov chain, whose stationary distribution captures the invariant properties of network plasticity. However convergence to this stationary… CONTINUE READING

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