The communication-through-coherence (CTC) hypothesis states that a sending group of neurons will have a particularly strong effect on a receiving group if both groups oscillate in a phase-locked (“coherent”) manner (Fries, 2005 , 2015 ). Here, we consider a situation with two visual stimuli, one in the focus of attention and the other distracting, resulting in two sites of excitation at an early cortical area that project to a common site in a next area. Taking a modeler’s perspective, we confirm the workings of a mechanism that was proposed by Bosman et al. ( 2012 ) in the context of providing experimental evidence for the CTC hypothesis: a slightly higher gamma frequency of the attended sending site compared to the distracting site may cause selective interareal synchronization with the receiving site if combined with a slow-rhythm gamma phase reset. We also demonstrate the relevance of a slightly lower intrinsic frequency of the receiving site for this scenario. Moreover, we discuss conditions for a transition from bottom-up to top-down driven phase locking.

An implementation of attentional bias is presented for a network model that couples excitatory and inhibitory oscillatory units in a manner that is inspired by the mechanisms that generate cortical gamma oscillations. Attentional biases are implemented as oscillatory coherences between excitatory units that encode the spatial location or features of the target and the pool of inhibitory units. This form of attentional bias is motivated by neurophysiological findings that relate selective attention to spike field coherence. Including also pattern recognition mechanisms, we demonstrate how this implementation of attentional bias leads to selection of an attentional target while suppressing distracters for cases of spatial and feature-based attention. With respect to neurophysiological observations, we argue that the recently found positive correlation between high firing rates and strong gamma locking with attention (Vinck, Womelsdorf, Buffalo, Desimone, & Fries, 2013 ) may point to an essential mechanism of the brain’s attentional selection and suppression processes.

Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration generates the desynchronization that is needed for self-organized segmentation of two overlapping patterns. In this letter, we continue the discussion of this remarkable feature, giving also an example with several overlapping patterns. Due to acceleration, Hebbian memory implies a frequency spectrum for pure pattern states, defined as coherent patterns with decoherent overlapping patterns. With reference to this frequency spectrum and related frequency bands, the process of pattern retrieval, corresponding to the formation of temporal coding assemblies, is described as resulting from constructive interference (with frequency differences due to acceleration) and phase locking (due to synchronization).

Temporal coding is considered with an oscillatory network model that generalizes the Cohen-Grossberg-Hopfield model. It is assumed that the frequency of oscillating units increases with stronger and more coherent input. We refer to this mechanism as acceleration. In the context of Hebbian memory, synchronization and acceleration take complementary roles, and their combined effect on the storage of patterns is profound. Acceleration implies the desynchronization that is needed for self-organized segmention of two overlapping patterns. The superposition problem is thereby solved even without including competition couplings. With respect to brain dynamics, we point to analogies with oscillation spindles in the gamma range and responses to perceptual rivalries.

Using an oscillatory network model that combines classical network models with phase dynamics, we demonstrate how the superposition catastrophe of pattern recognition may be avoided in the context of phase models. The model is designed to meet two requirements: on and off states should correspond, respectively, to high and low phase velocities, and patterns should be retrieved in coherent mode. Nonoverlapping patterns can be simultaneously active with mutually different phases. For overlapping patterns, competition can be used to reduce coherence to a subset of patterns. The model thereby solves the superposition problem.