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

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.: Our mind continuously adapts to background sensory events while preserving and even enhancing its sensitivity to deviant objects. In the visual modality, for instance, a target violating a surrounding pattern is easily detected, a phenomenon termed “pop-out.” Indeed, the automatic attention we pay to the irregular or the surprising, which developed as a valuable aid in our survival, is often used to advantage nowadays in popular culture and advertisement. Such phenomena have been investigated in many systems, from psychophysics in behaving animals to experiments in neural networks developing in vitro. In this work, we develop a mechanistic model that demonstrates how a relatively simple system may express such selective behavior. We apply our model first to the case of transiently responding networks, and compare the results with computer simulations and experimental data collected from neural networks developing in vitro. We also demonstrate the application of the model to other systems. Our approach provides insight as to how complex, behavior-related phenomena may arise from simple dynamic interactions between the system's elementary components.