Adaptation over multiple timescales is evident at every level of organization. Our actions are the result of the interactions between current external stimuli and a lifelong history. Likewise, the response of single neurons to a stimulus depends on a long stimulation history. This observation implies that a neural system is usually encountered in a different state each time it is stimulated or observed, and hence adaptation has profound implications both for decoding neural activity and for stimulating neural systems. Despite the ubiquity and importance of adaptation, it is still unknown how multiple temporal scales of adaptation interact across multiple levels of organization. Here, I aim to understand the interaction between timescales from three sources: those present in individual neurons, those emerging from neural networks, and those presented to a network through the environment. The key observation driving this research direction is that specific timescales are often present in all organizational levels, and therefore when moving from a neuron to a network we should not look for emergence of new timescales, but rather for a different way of using the same timescales. I am interested in exploring this “complexity from complexity” transformation by analyzing both the dynamics and the computational power of networks composed of elements with multiple timescales. Experimental data from my collaborators will be used to constrain and validate the models. By defining and exploring this new framework I hope to understand the interaction between timescales stemming from different sources and provide novel methods both to analyze neural data, and to stimulate neural systems.