NEV2lkit: a new open source tool for handling neuronal event files from multi-electrode recordings.

The analysis and discrimination of action potentials, or "spikes", is a central issue to systems neuroscience research. Here we introduce a free open source software for the analysis and discrimination of neural spikes based on principal component analysis and different clustering algorithms. The main objective is to supply a friendly user interface that links the experimental data to a basic set of routines for analysis, visualization and classification of spikes in a consistent framework. The tool has been tested on artificial data sets, on multi-electrode extracellular recordings from ganglion cell populations in isolated superfused mouse, rabbit and turtle retinas, and on electrophysiological recordings from mouse visual cortex. Our results show that NEV2lkit is very reliable and able to satisfy the experimental demands in terms of accuracy, efficiency and consistency across experiments. It performs fast unit sorting in single or multiple experiments and allows the extraction of spikes from over large time intervals in continuously recorded data streams. The tool is implemented in C++ and runs cross-platform on Linux, OS X and Windows systems. To facilitate the adaptation and extension as well as the addition of new routines, tools and algorithms for data analysis, the source code, binary distributions for different operating systems and documentation are all freely available at http://nev2lkit.sourceforge.net .

Easily made single-walled carbon nanotube surface microelectrodes for neuronal applications.

The present work examines the feasibility of a simple method for using single-walled carbon nanotubes (SWNT) to fabricate multielectrode arrays (MEA) for electrophysiological recordings. A suspension of purified SWNTs produced by arc discharged was directly deposited onto standard platinum electrodes. The in vitro impedance and electrochemical characterizations demonstrated the enhanced electrical properties of the SWNT microelectrode array. To test its functionality we performed extracellular ganglion cell recordings in isolated superfused rabbit retinas. Our results showed that SWNT based electrode arrays have potential advantages over metal electrodes and can be successfully used to record the single and multi-unit activity of ganglion cell populations.

Conditioned spikes: a simple and fast method to represent rates and temporal patterns in multielectrode recordings.

Increasing evidence suggests that the brain utilizes distributed codes that can only be analyzed by simultaneously recording the activity of multiple neurons. This paper introduces a new methodology for studying neural ensemble recordings. The method uses a novel representation to provide complementary information about the stimuli which are contained in the temporal pattern of the spike sequence. By using this procedure, a high correlation of synchronized events with stimuli times is apparent. To quantify the results and to compare the performance of this method against the most traditional raster plot, we have used Fano factor and cross-correlation analysis. Our results suggest that several consecutive spikes from different neurons within an extended time window may encode behaviorally relevant information. We propose that this new representation, in addition to the other approaches currently used (standard raster plots, multivariate statistical methods, neuronal networks, information theory, etc.), can be a useful procedure to describe population spike dynamics.

Retinal ganglion cell synchronization by fixational eye movements improves feature estimation.

Image movements relative to the retina are essential for the visual perception of stationary objects during fixation. Here we have measured fixational eye and head movements of the turtle, and determined their effects on the activity of retinal ganglion cells by simulating the movements on the isolated retina. We show that ganglion cells respond mainly to components of periodic eye movement that have amplitudes of roughly the diameter of a photoreceptor. Drift or small head movements have little effect. Driven cells that are located along contrast borders are synchronized, which reliably signals a preceding movement. In an artificial neural network, the estimation of spatial frequencies for various square wave gratings improves when timelocked to this synchronization. This could potentially improve stimulus feature estimation by the brain.