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Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation,

Summary: Selective Adaptation in Networks of
Heterogeneous Populations: Model, Simulation,
and Experiment
Avner Wallach1*
, Danny Eytan1
, Shimon Marom2
, Ron Meir1
1 Faculty of Electrical Engineering, Technion Israel Institute of Technology, Haifa, Israel, 2 Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
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 subpopulations 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 that 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.


Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Meir, Ron - Department of Electrical Engineering, Technion, Israel Institute of Technology


Collections: Computer Technologies and Information Sciences; Engineering