Abstract:
We considered the problem of determining how the retinal network may interact with electrical epiretinal stimulation in shaping the spike trains of ON and OFF ganglion cells, and thus the synaptic input to first–stage cortical neurons. This issue is fundamental for the development of retinal prosthesis image–encoding algorithms.
We developed a biophysical model of the retinal network with nine stacked neuronal mosaics. Neurons were modeled as leaky integrators with added membrane and synaptic conductances. A complete photoreceptor loss was assumed, with the remaining retinal circuitry having the generally accepted connections. Epiretinal stimulation via a planar disk electrode was modeled, and it was assumed that only cell bodies are stimulated by the extracellular potential gradient. Retinal output was assessed at the level of cortical (V1) simple cells which receive convergent but opponent input from ON and OFF ganglion cells.
Our simulations reveal that the input to V1 has complex spatiotemporal dynamics that are influenced by contributions from the entire retinal network. Electrical stimulation alone results in indiscriminate excitation of ON and OFF ganglion cells and a patchy input to the cortex with islands of excitation among regions of no net excitation. Activation of the retinal network biases the excitation of ON relative to OFF ganglion cells and, in addition, gradually interpolates and focuses the initial, patchy input to V1. As stimulation strength increases, the input to V1 spreads beyond the electrode contact. At very strong stimulation levels, the spike rates of ganglion cells saturate, resulting in significant distortion of the input to V1.
Our results agree with the "bright spot of light'' percepts, and with the "clusters of multiple discrete percepts" elicited in RP and AMD patients following epiretinal stimulation. The distortions in the spatial profile of the input to V1 during strong stimulation indicate significant limitations in the dynamic range of percepts that can be achieved with an epiretinal prosthesis.
Keywords: computational modeling • retinal connections, networks, circuitry