Empirical Models as a Basis for Synthesis of Large Spiking Neural Networks with Pre-Specified Properties
Mikhail V. Kiselev · 2014
Analysis of behaviour of large neuronal ensembles using mean-field equations and similar approaches was an important instrument in theory of spiking neural networks during almost all its history. However, it often implies dealing with complex systems of integro-differential equations which are very hard not only for obtaining explicit analytical solution but also for simpler tasks like stability analysis. Building empirical models on the basis of experimental data gathered in process of simulation of small size networks is considered in the paper as a practical alternative to these traditional methods. A methodology for creation and verification of such models using decision trees, multiple adaptive regression splines and other data mining algorithms is discussed. This idea is illustrated by the two examples – prediction of probability of avalanche-like excitation growth in the network and analysis of conditions necessary for development of strong firing frequency oscillations.