Multi-population approach to approximate the development of neocortical networks

Cultured natural cortical neurons form functional networks through a complex set of developmental steps during the first weeks in vitro. The dynamic behavior of the network in this early development period changes from spontaneous spiking of single neurons to slow synchronous activity and finally to a mature firing profile with complex high-order patterns of spikes and bursts. In the present modeling study we investigate the required properties of the networks during the development by biologic realistic simulations and use an evolutionary algorithm (EA) to fit the parameters to the results of biological experiments. For each day in vitro (DIV) during the development a population of individuals is defined, which determines the statistical parameters to generate the networks and set up neuron properties by genes. The fitness function and the recombination algorithm are extended for this multi-population approach to allow the EA to follow different parameter trajectories over time (which are possible solutions) and include several kinds of biologically a-priori knowledge with an adjustable uncertainty.