Spike history neural response model

Kameneva, Tatiana; Abramian, Miganoosh; Zarelli, Daniele; Nešić, Dragan; Burkitt, Anthony N.; Meffin, Hamish; Grayden, David B.

doi:10.1007/s10827-015-0549-5

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…A similar model has been employed previously to model the spatio-temporal dynamics of neurons responding to visual stimulation of the retina (DeAngelis et al 1993). We expect that by modifying our model to include a temporal dimension in our linear filters, as by Chichilnisky (2001) and Kameneva et al (2015), the model could be extended to predict temporal dynamics of cortical responses during repetitive, high-rate stimulation, such as perceptual fading. While there is strong evidence suggesting that perceptual fading could be attributed to desensitization observed in RGCs (Freeman and Fried 2011), it could also be attributed to neural mechanisms within central visual structures.…”

Section: Considerations For Future Applicationsmentioning

confidence: 99%

Prediction of cortical responses to simultaneous electrical stimulation of the retina

et al. 2016

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We have demonstrated a simple model that accurately describes cortical responses to simultaneous stimulation of a suprachoroidal retinal prosthesis. Overall, our results demonstrate that cortical responses to simultaneous multi-electrode stimulation of the retina are repeatable and predictable, and that interactions between electrodes during simultaneous stimulation are predominantly linear. The model shows promise for determining optimal stimulation paradigms for exploiting interactions between electrodes to shape neural activity, thereby improving outcomes for patients with retinal prostheses.

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Section: Considerations For Future Applicationsmentioning

confidence: 99%

Prediction of cortical responses to simultaneous electrical stimulation of the retina

et al. 2016

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“…Generally, STA and STC models make use of white noise inputs and have the advantage that a wide repertoire of possible inputs patterns can be explored. White noise models have previously been explored to describe the temporal properties of electrical stimulation in the retina [ 26 , 27 ]. Spatial interactions between stimulating electrodes has not been previously investigated.…”

Section: Introductionmentioning

confidence: 99%

A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

et al. 2016

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Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy.

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“…Control policies can be implemented by classification, model independent, or model based approaches (Kameneva et al, 2015 ).…”

Section: Control Algorithmsmentioning

confidence: 99%

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

et al. 2016

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It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.

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Spike history neural response model

Cited by 6 publications

References 34 publications

Prediction of cortical responses to simultaneous electrical stimulation of the retina

Prediction of cortical responses to simultaneous electrical stimulation of the retina

A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

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