Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation and Experiment
Avner Wallach, Danny Eytan, Shimon Marom and Ron Meir
PLOS Computational Biology
Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various sub-populations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions which were corroborated in both computer simulations and in cultures of cortical neurons developing in-vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases.