Biophysical basis of the linear electrical receptive fields of retinal ganglion cells. (2nd July 2018) - Esler, Timothy B; Maturana, Matias I; Kerr, Robert R; Grayden, David B; Burkitt, Anthony N; Meffin, Hamish
Abstract: Responses of retinal ganglion cells to direct electrical stimulation have been shown experimentally to be well described by linear–nonlinear models. These models rely on the simplifying assumption that retinal ganglion cell responses to stimulation with an array of electrodes are driven by a simple linear weighted sum of stimulus current amplitudes from each electrode, known as the 'electrical receptive field'. Objective . This paper aims to demonstrate the biophysical basis of the linear–nonlinear model and the electrical receptive field to facilitate the development of improved stimulation strategies for retinal implants. Approach . We compare the linear–nonlinear model of subretinal electrical stimulation with a multi-layered, biophysical, volume conductor model of retinal stimulation. Main results . Our results show that the linear electrical receptive field of the linear–nonlinear model matches the transmembrane currents induced by electrodes (the activating function) at the site of the high-density sodium channel band with only minor discrepancies. The discrepancies are mostly eliminated by including axial current flow originating from adjacent cell compartments. Furthermore, for cells where a single linear electrical receptive field is insufficient, we show that cell responses are likely driven by multiple sites of action potential initiation with multiple distinct receptive fields, each of which can be accurately described by the activating function. Significance . This result establishes that the biophysical basis of the electrical receptive field of the linear–nonlinear model is the superposition of transmembrane currents induced by different electrodes at and near the site of action potential initiation. Together with existing experimental support for linear–nonlinear models of electrical stimulation, this provides a firm basis for using this much simplified model to generate more optimal stimulation patterns for retinal implants.