Abstract: We have developed a toolbox and graphic user interface, EEGLAB, running under the cross-platform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive ‘pop ’ functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A ‘plug-in ’ facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available

Abstract—Relaxation oscillators arise frequently in physics, electronics, mathematics, and biology. Their mathematical definitions possess a high degree of flexibility in the sense that through appropriate parameter choices relaxation oscillators can be made to exhibit qualitatively different kinds of oscillations. We study numerically four different classes of relaxation oscillators through their synchronization rates in one-dimensional chains with a Heaviside step function interaction and obtain the following results. Relaxation oscillators in the sinusoidal and relaxation regime both exhibit an average time to synchrony, , where is the chain length. Relaxation oscillators in the singular limit exhibit, where is a numerically obtained value less than 0.5. Relaxation oscillators in the singular limit with parameters modified so that they resemble spike oscillations exhibit ��� @ A in chains and ��� @ A in two-dimensional square networks of length. Finally, using a sigmoid interaction results in P, for relaxation oscillators in the sinusoidal and relaxation regimes, indicating that the form of the coupling is a controlling factor in the synchronization rate. Index Terms—Coupled oscillators, neural dynamics, relaxation oscillators, synchronization rate, synchrony.

Copyright (c) 2003 by the American Physiological Society. Nase et al., Neuronal synchrony in mouse visual cortex JN-00480-2002.R1 2 Synchronization of neuronal discharges has been hypothesized to play a role in defining cell assemblies representing particular constellations of stimulus features. In many systems and species, synchronization is accompanied by an oscillatory response modulation at frequencies in the gamma-band (>30Hz). The cellular mechanisms underlying these phenomena of synchronization and oscillatory patterning have been studied mainly in vitro due to the difficulty in designing a direct in vivo assay. With the prospect of utilizing conditional genetic manipulations of cortical network components, our objective was to test whether the mouse would meet the criteria to provide a model system for the study of gamma-band synchrony. Multi-unit and local field potential recordings were made from the primary visual cortex of anesthetized C57BL/6J mice. Neuronal responses evoked by moving gratings, bars and random dot patterns were analyzed with respect to neuronal synchrony and temporal patterning. Oscillations at