Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures

Abstract

Reproducing high-acuity vision with an epiretinal implant will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without measuring them directly. Here we demonstrate an approach that exploits intrinsic electrical features of primate RGCs. First, ON-parasol and OFF-parasol RGCs were identified with 95% accuracy using electrical features. Then, the somatic electrical footprint, predicted cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. Across five retinas, these models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses, an upper bound. This finding, and linear decoding of images from predicted RGC activity, suggested that the inference approach may be useful for high-fidelity sight restoration.

High-fidelity reproduction of visual signals by electrical stimulation in the central primate retina

“…4D,E), as has been demonstrated previously in the peripheral retina (Fig. 4F,G, (Richard et al, 2016; Madugula et al, 2022a; Zaidi et al, 2022)). However, it is unclear how effective this method will be in a degenerated retina.…”

“…Axon spike conduction velocity was estimated from the electrical images of ON and OFF parasol and midget cells, as described previously (Madugula et al, 2022a; Zaidi et al, 2022). Briefly, axonal electrodes for each cell were identified (see above) and pairwise velocity estimates were computed by dividing the distance between each electrode by the time difference in the negative peak amplitude, for each pair of axonal electrodes.…”

“…Axon spike conduction velocities and the spike train autocorrelation function were used together to distinguish ON and OFF parasol and ON and OFF midget cells, to demonstrate the effectiveness of cell type classification based on intrinsic electrical features alone (Richard et al, 2016; Zaidi et al, 2022). Each preparation in this analysis (Fig.…”

Precise control of neural activity using dynamically optimized electrical stimulation

“…First, new surgical methods must be developed to implant a tiny chip on the surface of the retina with stable contact. Second, receptive field locations, cell types and reconstruction filters must be inferred using electrical features rather than light-evoked responses in a blind retina (Sekirnjak et al 2011; Li et al 2015; Richard, Goetz, and Chichilnisky 2015; Zaidi et al 2022). Third, the stimulation approach must be modified to account for changes in spontaneous/oscillatory activity in the degenerated retina (Sekirnjak et al 2011; Goo et al 2015; Trenholm and Awatramani 2015).…”

“…In a clinical implant, this would not be possible. Previous work has shown that distinct cell types can be identified from spontaneous electrical activity (Richard, Goetz, and Chichilnisky 2015; Zaidi et al 2022)…”