There are many potential applications of randomness in modeling. These fall into two broad categories:
- Randomization of the model specification
- Purpose: to emulate natural biological variation of parameters such as number of cells, connectivity between cells, anatomical and/or biophysical properties of cells and their connections.
- Randomization of simulation execution
- Purpose: to emulate stochastic phenomena such as single channel gating, transmitter release, afferent spike trains, variation of natural stimuli, extrinsic noise sources.
Recurring issues in the specification of models of cells and networks that involve randomness include the following:
- How to create model specification code that employs randomization in a way that (1) avoids undesired correlations between parameters, and (2) produces a model cell or network that has the same architecture and biophysical properties, and generates the same simulation results regardless of whether it is run on serial or parallel hardware, the number of processors that it uses, and how the elements of the model are distributed over the processors.
- How to generate spike streams or other signals that fluctuate in ways that are statistically independent of each other.
These papers present models implemented with NEURON that provide some examples of randomization in model specification code:
- The network with random connectivity in
Hines, M.L. and Carnevale, N.T. Translating network models to parallel hardware in NEURON. J. Neurosci. Methods 169:425-455, 2008.
Preprint available from http://www.neuron.yale.edu/neuron/nrnpubs/, source code available via accession number 96444 from ModelDB http://modeldb.yale.edu/ - The network models in
Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Goodman, P.H., Harris, F.C.J., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A., El Boustani, S., and Destexhe, A. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23:349-398, 2007.
Preprint available from http://www.neuron.yale.edu/neuron/nrnpubs/, source code available via accession number 83319 from ModelDB http://modeldb.yale.edu/
Interested users are encouraged to read these papers, and download and analyze the related source code, in order to better understand how to apply randomization in the construction and simulation of models.