Phase transition and other phenomena in chains of coupled oscilators
SIAM Journal on Applied Mathematics
Neural Computation
Synchrony in excitatory neural networks
Neural Computation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
On population-based simulation for static inference
Statistics and Computing
Type i membranes, phase resetting curves, and synchrony
Neural Computation
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
| Hi-index | 0.00 |
Phase response curve (PRC) of an oscillatory neuron describes the response of the neuron to external perturbation. The PRC is useful to predict synchronized dynamics of neurons; hence, its measurement from experimental data attracts increasing interest in neural science. This paper introduces a Bayesian method for estimating PRCs from data, which allows for the correlation of errors in explanatory and response variables of the PRC. The method is implemented with a replica exchange Monte Carlo technique; this avoids local minima and enables efficient calculation of posterior averages. A test with artificial data generated by the noisy Morris-Lecar equation shows that the proposed method outperforms conventional regression that ignores errors in the explanatory variable. Experimental data from the pyramidal cells in the rat motor cortex is also analyzed with the method; a case is found where the result with the proposed method is considerably different from that obtained by conventional regression.