Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

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Quantifying the amount and content of information transfer between neural populations is crucial to understand brain dynamics and cognitive functions. Most data-driven methods exploit the notion of Wiener-Granger causality, a statistical concept based on temporal prediction.Transfer Entropy and Directed Information formalise this notion by means of information theoretical quantities and can capture any (linear and nonlinear) time-lagged conditional dependencies, thus quantifying the amount of information flow between neural signals.Nevertheless, none of these metrics can reveal what type of information is exchanged. To address this issue, we developed a new measure called Feature-specific Information Transfer (FIT) that is able to quantify both the amount and content of information transfer between neuronal signals. We tested the novel metric on simulated data and showed that it successfully captures feature-specific information transfer in different communication scenarios including feedforward communication, external confounding inputs and synergistic interactions. Moreover, the FIT measure displayed sensitivity to modulations in temporal parameters of information transfer and signal-to-noise ratios, and correctly inferred the directionality of transfer between signals. We then tested FIT's ability to track feature-specific information flow from neurophysiological data. First, we analysed human electroencephalographic data acquired during a face detection task and confirmed current hypotheses suggesting that information about the presence of an eye in a face image flows from the contralateral to the ipsilateral hemisphere with respect to the position of the eye. Second, we analysed multi-unit activity data recorded from thalamus and cortex of rat's brain, and showed that the FIT measure successfully detected bottom-up information transfer about visual or somatosensory stimuli in the corresponding neural pathway. Third, we analysed cortical high-gamma activity estimated from human magnetoencephalographic data during visuomotor mapping, and confirmed the notion that visuomotor-related information flows from superior parietal to premotor areas. Altogether our work suggests that the FIT measure has the potential to uncover previously hidden feature-specific information transfer from neural data and provide a better understanding of brain communication.