Bayesian Inference of Functional Connectivity and Network Structure From Spikes

@article{Stevenson2009BayesianIO,
  title={Bayesian Inference of Functional Connectivity and Network Structure From Spikes},
  author={I. H. Stevenson and J. M. Rebesco and N. G. Hatsopoulos and Zenku Haga and L. E. Miller and K. P. Kording},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  year={2009},
  volume={17},
  pages={203-213}
}
Current multielectrode techniques enable the simultaneous recording of spikes from hundreds of neurons. To study neural plasticity and network structure it is desirable to infer the underlying functional connectivity between the recorded neurons. Functional connectivity is defined by a large number of parameters, which characterize how each neuron influences the other neurons. A Bayesian approach that combines information from the recorded spikes (likelihood) with prior beliefs about functional… CONTINUE READING

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