Theta oscillons in behaving rats.

Abstract

Recently discovered constituents of the brain waves-the oscillons-provide a high-resolution representation of the extracellular field dynamics. Here, we study the most robust, highest-amplitude oscillons recorded in actively behaving male rats, which underlie the traditional θ-waves. The resemblances between θ-oscillons and the conventional θ-waves are manifested primarily at the ballpark level-mean frequencies, mean amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the θ-rhythms' structure, origins and functions. In particular, we demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a weak noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the oscillons. The results suggest that the synchronicity levels in physiological networks are fairly low and are modulated by the animal's physiological state.Significance statement Oscillatory extracellular fields modulate neural activity at multiple spatiotemporal scales and hence play major roles in physiology and cognition. Traditionally, these fields' organization is described via harmonic decompositions into θ, γ and other "brain waves." Here we argue that these constructs are only approximations to the physical oscillatory motifs-the oscillons, which represent the actual temporal architecture of synchronized neural dynamics. Focusing on the low-frequency θ-oscillons, we demonstrate correspondences with the traditional θ-waves for averaged, lento-changing characteristics, and discuss several new properties and dynamics that heretofore remained unexplored. Specifically, speed-coupled frequency modulations support oscillatory models of brain wave dynamics, suggesting a novel, "FM" perspective on the information exchange in hippocampo-cortical network and linking electrophysiological data to theoretical models of neuronal synchronization.