A Measure of Concurrent Neural Firing Activity Based on Mutual Information.

Multiple methods have been developed in an attempt to quantify stimulus-induced neural coordination and to understand internal coordination of neuronal responses by examining the synchronization phenomena in neural discharge patterns. In this work we propose a novel approach to estimate the degree of concomitant firing between two neural units, based on a modified form of mutual information (MI) applied to a two-state representation of the firing activity. The binary profile of each single unit unfolds its discharge activity in time by decomposition into the state of neural quiescence/low activity and state of moderate firing/bursting.

Measures of spike train synchrony for data with multiple time scales.

Background: Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset.

New Method: In data containing multiple time scales (e.

Time-frequency characterization of local field potential in a decision making task.

This study seeks to characterize the neuronal mechanisms underlying voluntary decisions to check/verify. In order to describe and potentially decode decisions from brain signals we analyzed intracortical recordings from monkey prefrontal regions obtained during a cognitive task requiring self-initiated as well as cue-instructed decisions. Using local field potentials (LFP) and single units, we analyzed power spectral density, oscillatory modes, power profiles in time, single unit firing rate, and spike-phase relationships in the β band.

SPIKY: a graphical user interface for monitoring spike train synchrony.

Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales.