  Changes in an animal's behavioral state, such as arousal and movements,
induce {complex modulations of the baseline input currents to sensory areas,
eliciting sensory modality-specific effects. A simple computational principle
explaining the effects of baseline modulations to recurrent cortical circuits
is lacking. We investigate the benefits of baseline modulations using a
reservoir computing approach in recurrent neural networks with random
couplings. Baseline modulations unlock a set of new network phases and
phenomena, including chaos enhancement, neural hysteresis and ergodicity
breaking. Strikingly, baseline modulations enable reservoir networks to perform
multiple tasks, without any optimization of the network couplings.} Baseline
control of network dynamics opens new directions for brain-inspired artificial
intelligence and sheds new light on behavioral modulations of cortical
activity.
