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Cited by 2Neuroscience and Neural EngineeringTactile and Sensory InteractionsAnalog and Mixed-Signal Circuit Design

Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants

Vasireddy, Praful K., Gogliettino, Alex R., Brown, Jeff B., Vilkhu, Ramandeep S., Madugula, Sasi S., Phillips, A. J., Mitral, Subhasish, Hottowy, Pawel +4 more·2023·2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)
DOI 10.1109/ner52421.2023.10123907
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Abstract

Epiretinal implants are designed to restore visual function by direct stimulation of retinal ganglion cells (RGCs) with a multi-electrode array. However, the efficacy of present-day epiretinal implants is limited by indiscriminate, simultaneous activation of many RGCs of different types that normally convey distinct visual information, resulting in highly unnatural visual signals. Even with high-density multi-electrode arrays in a laboratory setting, single-electrode stimulation often cannot target an individual cell selectively. A possible solution is to use spatially patterned multi-electrode stimulation. Previous studies have demonstrated enhanced selectivity using such stimuli, but a general framework for characterizing the responses of RGCs to stimulation by multiple electrodes has yet to be realized. Here we propose a generalizable model for the response of a neuron to multi-electrode stimuli, and demonstrate that it can be used to systematically and efficiently improve the selectivity of epiretinal stimulation. The efficacy of the model and of multi-electrode stimuli is validated using three-electrode triplet stimulation. The model assumes that there are several possible activation sites on each cell where a spike can originate, and that the probability of activation at each site is determined by a weighted sum of the currents applied on the electrodes. The multi-site model provides excellent fits to experimental data as well as an interpretable mechanism of multi-electrode activation. Additionally, since the model has few parameters, it permits efficient closed-loop calibration. An optimal experimental design is presented and validated for quickly learning the model parameters and identifying multi-electrode stimuli that are more selective than can be achieved with single-electrode stimuli. The model and closed-loop algorithm could potentially be used to optimize the selectivity of other neural interfaces.

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  • Alternative stimulation strategies have not been validated in vivo in humans using perceptually relevant pulse-train stimulation, and most do not address the substantial day-to-day variability in cortical responses that limits clinical reliability.

    “While alternative stimulation strategies have been proposed [25–39], none have been validated in vivo in humans using perceptually relevant pulse-train stimulation, and most do not address the substantial day-to-day variability in cortical responses that limits clinical reliability.”

    From Deep Learning–Based Control of Electrically Evoked Activity in Human Visual Cortex (2025) · cites this paper as [29]

  • Although the greedy algorithm is nearly optimal for a single-electrode dictionary, the reconstruction performance of an artificial retina could be improved by enhancing the dictionary (e.g. by using calibrated, optimized multi-electrode stimulation patterns).

    “Thus, although the greedy algorithm is nearly optimal for a single-electrode dictionary, the reconstruction performance of an artificial retina could be improved by enhancing the dictionary (e.g. by using calibrated, optimized multi-electrode stimulation patterns; Jepson et al., 2014; Vasireddy et al., 2023).”

    From Precise control of neural activity using dynamically optimized electrical stimulation (2024) · cites this paper as Vasireddy et al., 2023

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Authors
Vasireddy, P. K., Gogliettino, A. R., Brown, J. B., Vilkhu, R. S., Madugula, S. S., Phillips, A. J., Mitral, S., Hottowy, P., Sher, A., Litke, A., Shah, N. P., & Chichilnisky, E. J.
Affiliations
  • Stanford University (US)
  • Stanford University
  • AGH University of Science and Technology
  • University of California, Santa Cruz (US)
ORCID
Venue
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)
Publisher
IEEE
Published
2023-04-24
Pages
1-4
ISSN
1948-3554 (ISSN-L)
DOI
10.1109/ner52421.2023.10123907
OpenAlex
W4377089391
Cited by
2
References
22
Citations by year
2026: 1 · 2024: 1
FWCI
2.39
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International IEEE/EMBS Conference on Neural Engineering (publishedVersion)
Funders
  • NSF (1828993)
  • NIH (EY021271 (EJC))
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
  • Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
Keywords
epiretinal implants, multi-electrode stimulation, retinal ganglion cells, stimulation selectivity, closed-loop calibration, optimal experimental design, neural interfaces, visual prosthesis

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