Figures
Nerve cell on worn paper.
This image illustrates a B4 neuron from the mollusk Lymnaea stagnalis on a background of mathematical expressions, simulation data, and binary digits, which represent the implicit assumption that nervous systems can be modeled and simulated in a digital computer. In this issue, Vavoulis et al. (10.1371/journal.pcbi.1002401) present a method for estimating dynamic states and parameters in data-driven models of neurons and neural networks based on principles of Bayesian statistics and Sequential Monte Carlo simulation.
Image Credit: Dimitrios V. Vavoulis and Volko A. Straub, the Universities of Warwick and Leicester, United Kingdom, 2012.
Citation: (2012) PLoS Computational Biology Issue Image | Vol. 8(3) March 2012. PLoS Comput Biol 8(3): ev08.i03. https://doi.org/10.1371/image.pcbi.v08.i03
Published: March 29, 2012
Copyright: © 2012 Vavoulis et al. . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This image illustrates a B4 neuron from the mollusk Lymnaea stagnalis on a background of mathematical expressions, simulation data, and binary digits, which represent the implicit assumption that nervous systems can be modeled and simulated in a digital computer. In this issue, Vavoulis et al. (10.1371/journal.pcbi.1002401) present a method for estimating dynamic states and parameters in data-driven models of neurons and neural networks based on principles of Bayesian statistics and Sequential Monte Carlo simulation.
Image Credit: Dimitrios V. Vavoulis and Volko A. Straub, the Universities of Warwick and Leicester, United Kingdom, 2012.