The brain is a self-organizing system, which has evolved such that neuronal responses and related behavior are continuously adapted with respect to the external and internal context. This powerful capability is achieved through the modulation of neuronal interactions depending on the history of previously processed information. The resulting connectivity changes, together with stochastic processes (i.e. noise) influence ongoing neuronal dynamics. The role of such state-dependent fluctuations may be one of the fundamental computational properties of the brain, being pervasively present in human behavior and leaving a distinctive fingerprint in neuroscience data. This development will be captured by the Research Topic "State-Dependent Brain Computation". It will provide an account of prevailing concepts and theories plus recent advances on the role of ongoing brain dynamics - reflecting experiences, global brain states, context and noise - for task-related information processing. Works from the conceptual, experimental and computational-modeling domains will be presented, focusing on the following two issues: 1. Generative mechanisms of ongoing neuronal dynamics, and 2. Principles of interaction between ongoing dynamics and incoming events or tasks. The entire range of spatial and temporal scales encountered in brain dynamics will be covered, i.e. from microscopic molecular to macroscopic population dynamics and from fast processes evolving within milliseconds to slow ones taking hours or longer. Also the degree of abstraction in the presented modeling work will vary, ranging from simplified but biophysically plausible network models to highly detailed neuron models. By putting the different mathematical and empirical aspects in this mutual context, this Research Topic aims to elucidate the principle mechanisms of state dependent neuronal processing.
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