Do neural oscillations modulate information processing in the brain? From routing states to computing modes

by Demian Battaglia (AIx-Marseille University, Institute for System Neuroscience)

Monday 26 Feb 2018, 14:30 → 15:30 Europe/Rome

Sala Cortini (Dipartimento di Fisica - Ed. E. Fermi)

Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying rapid information rerouting between such microcircuits are still unknown. It has been proposed based on growing experimental evidence that changing patterns of coherence between local gamma rhythms support flexible information rerouting. The stochastic and transient nature of gamma oscillations in vivo, however, is hard to reconcile with such a function, as other experiments have shown in a seemingly contradictory way. Here we show through a computational modelling approach that models of cortical circuits near the onset of oscillatory synchrony are well able to selectively route input signals despite the short duration of gamma bursts and the irregularity of neuronal firing. In canonical multiarea circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organize information flow by large-scale routing states. Specific self-organized routing states can be induced by minor modulations of background activity. Moving then to the analysis of electrophysiological recordings in anaesthetized and sleeping rats, we investigate whether changing oscillatory states may have an impact on ongoing information processing, beyond information routing. We are able to identify a multiplicity of internal “computing modes”, characterized by the flexible recruitment of alternative hub neurons, specialised in distinct elementary information processing functions (storage and transfer). We then characterize the discrete transitions spontaneously occurring between these modes. We find that switching between oscillatory states largely constrains which computing modes can be observed (theta-vs slow-oscillation specific modes). Furthermore, switching transitions assemble into sequences whose complexity is quantitatively and consistently measured to be larger during theta than slow oscillation epochs. Thus, changes of the oscillatory mode impact on both the “dictionary” within which computing modes are sampled and the “grammar” generating transitions between these modes.