Attractor Metadynamics in Adapting Neural Networks [chapter]

Claudius Gros, Mathias Linkerhand, Valentin Walther
2014 Lecture Notes in Computer Science  
Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a
more » ... ess which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuoustime autonomous model networks. We find both first-and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics.
doi:10.1007/978-3-319-11179-7_9 fatcat:rv6ow5jkvzae7c4kxnyvk26bee