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Markus Bongard

Publications and source records attributed to Markus Bongard.

2 recordsLinked to original sources

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.

Action Potentials↗

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.

Animals↗