Synchronization through uncorrelated noise in excitatory-inhibitory networks

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modelling studies point towards an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that synaptic noise can have a supporting role in facilitating inter-regional communication and that noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

A technical note on the geometric effects of volume to surface mapping of fMRI data

In this technical report we identify and report a problem with the process of volume to surface mapping that has potentially impacted studies that focus upon local neighborhood connectivity. We show that neighborhood correlations vary spatially with anatomical patterns (gyral structure) even when the underlying volumetric data is uncorrelated noise. We explore the effects of this anomaly across varying data resolutions and surface mesh densities. We finally propose an approach to mitigate these unwanted effects.

Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information

Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target’s position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter’s performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.

Coupled Map Lattices as Musical Instruments

We use one-dimensional coupled map lattices (CMLs) to generate sounds that reflect their spatial organization and temporal evolution from a random initial configuration corresponding to uncorrelated noise. In many instances, the process approaches an equilibrium, which generates a sustained tone. The pitch of this tone is proportional to the lattice size, so the CML behaves like an instrument that could be tuned. Among exceptional cases, we provide an example with a metastable strange attractor, which produces an evolving sound reminiscent of drone music.