Topographic prominence discriminator for the detection of short-latency spikes of retinal ganglion cells

Objective. Direct stimulation of retinal ganglion cells in degenerate retinas by implanting epi-retinal prostheses is a recognized strategy for restoration of visual perception in patients with retinitis pigmentosa or age-related macular degeneration. Elucidating the best stimulusresponse paradigms in the laboratory using multielectrode arrays (MEA) is complicated by the fact that the short-latency spikes (within 10 ms) elicited by direct retinal ganglion cell (RGC) stimulation are obscured by the stimulus artifact which is generated by the electrical stimulator. Approach. We developed an artifact subtraction algorithm based on topographic prominence discrimination, wherein the duration of prominences within the stimulus artifact is used as a strategy for identifying the artifact for subtraction and clarifying the obfuscated spikes which are then quantified using standard thresholding. Main results. We found that the prominence discrimination based filters perform creditably in simulation conditions by successfully isolating randomly inserted spikes in the presence of simple and even complex residual artifacts. We also show that the algorithm successfully isolated short-latency spikes in an MEA-based recording from degenerate mouse retinas, where the amplitude and frequency characteristics of the stimulus artifact vary according to the distance of the recording electrode from the stimulating electrode. By ROC analysis of false positive and false negative first spike detection rates in a dataset of one hundred and eight RGCs from four retinal patches, we found that the performance of our algorithm is comparable to that of a generally- used artifact subtraction filter algorithm which uses a strategy of local polynomial approximation (SALPA). Significance. We conclude that the application of topographic prominence discrimination is a valid and useful method for subtraction of stimulation artifacts with variable amplitudes and shapes. We propose that our algorithm may be used as stand-alone or supplementary to other artifact subtraction algorithms like SALPA.