The model is parameterized with measurement data in time-domain using a hybrid Particle Swarm Nelder Mead (PSO NM) optimization algorithm.
These models can be parameterized with empirical data and applied to real metapopulations for the purposes of research, management, and conservation.
SPOMs are also good models for real metapopulations living in highly fragmented landscapes, to the extent that they can be parameterized with empirical data and turned into tools that hold substantial promise for conservation, landscape planning, and management.
We use a model of FD that is parameterized with experimental data from a hippocampal basket cell and pyramidal cell connection, for fixed frequency input spikes at frequencies in the range of theta (3 8 Hz) and gamma (20 100 Hz) oscillations.
We use a model of FD that was parameterized with experimental data from dual whole-cell recordings from a presynaptic parvalbumin-positive (PV) basket cell (BC) connected to a postsynaptic CA1 (Cornu Ammonis 1 subregion) pyramidal cell, for family of fixed frequency input spikes into the presynaptic PV BC [1].
Using the relationship observed between mean annual temperature and root mass fraction, the model used to determine a climate-derived estimate of belowground C was parameterized with global data compiled from various sources including Usoltsev [37], Luo et al. [38], Cannell [39], and from over 1000 additional forest stands including the US [8].
Using the relationship observed between mean annual temperature and root mass fraction, the model used to determine a climate-derived estimate of belowground C was parameterized with global data compiled from various sources including Usoltsev [ 37], Luo et al. [ 38], Cannell [ 39], and from over 1000 additional forest stands including the US [ 8].
All models are parameterized with straightforward engineering data.
Once validated with an empirical potential, these laws are parameterized with ab initio data.
The model is parameterized with eddy covariance data.
