PubMed Health⌕ Search

Biomedical subjects

Martin Greschner

Publications and source records attributed to Martin Greschner.

3 recordsLinked to original sources

Complex spike-event pattern of transient ON-OFF retinal ganglion cells.

ON-OFF transient ganglion cells of the turtle retina show distinct spike-event patterns in response to abrupt intensity changes, such as during saccadic eye movements. These patterns consist of two main spike events, with the latency of each event showing a systematic dependency on stimulus contrast. Whereas the latency of the first event decreases monotonically with increasing contrast, as expected, the second event shows the shortest latency for intermediate contrasts and a longer latency for high and low contrasts. These spike-event patterns improve the discrimination of different light-intensity transitions based on ensemble responses of the ON-OFF transient ganglion cell subpopulation. Although the discrimination results are far better than chance using either spike counts or latencies of the first spikes, they are further improved by using properties of the second spike event. The best classification results are obtained when spike rates and latencies of both events are considered in combination. Thus spike counts and temporal structure of retinal ganglion cells carry complementary information about the stimulus condition, and thus spike-event patterns could be an important aspect of retinal coding. To investigate the origin of the spike-event patterns in retinal ganglion cells, two computational models of retinal processing are compared. A linear-nonlinear model consisting of separate filters for on and off response components fails to reproduce the spike-event patterns. A more complex cascade filter model, however, accurately predicts the timing of the spike events by using a combination of gain control loop and spike rate adaptation.

Algorithms↗

The temporal structure of transient ON/OFF ganglion cell responses and its relation to intra-retinal processing.

A subpopulation of transient ON/OFF ganglion cells in the turtle retina transmits changes in stimulus intensity as series of distinct spike events. The temporal structure of these event sequences depends systematically on the stimulus and thus carries information about the preceding intensity change. To study the spike events' intra-retinal origins, we performed extracellular ganglion cell recordings and simultaneous intracellular recordings from horizontal and amacrine cells. Based on these data, we developed a computational retina model, reproducing spike event patterns with realistic intensity dependence under various experimental conditions. The model's main features are negative feedback from sustained amacrine onto bipolar cells, and a two-step cascade of ganglion cell suppression via a slow and a fast transient amacrine cell. Pharmacologically blocking glycinergic transmission results in disappearance of the spike event sequence, an effect predicted by the model if a single connection, namely suppression of the fast by the slow transient amacrine cell, is weakened. We suggest that the slow transient amacrine cell is glycinergic, whereas the other types release GABA. Thus, the interplay of amacrine cell mediated inhibition is likely to induce distinct temporal structure in ganglion cell responses, forming the basis for a temporal code.

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↗