Biology subjects

Gogliettino, A. R.

Publications and source records attributed to Gogliettino, A. R..

11 recordsLinked to original sources

bioRxiv2024

Understanding responses to multi-electrode epiretinal stimulation using a biophysical model

ObjectiveNeural interfaces are designed to evoke specific patterns of electrical activity in populations of neurons by stimulating with many electrodes. However, currents passed simultaneously through multiple electrodes often combine nonlinearly to drive neural responses, making evoked responses difficult to predict and control. This response nonlinearity could arise from the interaction of many excitable sites in each cell, any of which can produce a spike. However, this multi-site activation hypothesis is difficult to verify experimentally. ApproachWe developed a biophysical model to study retinal ganglion cell (RGC) responses to multi-electrode stimulation and validated it using data collected from ex vivo preparations of the macaque retina using a microelectrode array (512 electrodes; 30{micro}m pitch; 10{micro}m diameter). ResultsFirst, the model was validated by using it to reproduce essential empirical findings from single-electrode recording and stimulation, including recorded spike voltage waveforms at multiple locations and sigmoidal responses to injected current. Then, stimulation with two electrodes was modeled to test how the positioning of the electrodes relative to the cell affected the degree of response nonlinearity. Currents passed through pairs of electrodes positioned near the cell body or far from the axon (>40 {micro}m) exhibited approximately linear summation in evoking spikes. Currents passed through pairs of electrodes close to the axon summed linearly when their locations along the axon were similar, and nonlinearly otherwise. Over a range of electrode placements, several distinct, localized spike initiation sites were observed, and the number of these sites covaried with the degree of response nonlinearity. Similar trends were observed for three-electrode stimuli. All of these trends in the simulation were consistent with experimental observations. Significance. These findings support the multi-site activation hypothesis for nonlinear activation of neurons, providing a biophysical interpretation of previous experimental results and potentially enabling more efficient use of multi-electrode stimuli in future neural implants.

bioengineering↗

bioRxiv2024

Modeling responses of macaque and human retinal ganglion cells to natural images using a convolutional neural network

Linear-nonlinear (LN) cascade models provide a simple way to capture retinal ganglion cell (RGC) responses to artificial stimuli such as white noise, but their ability to model responses to natural images is limited. Recently, convolutional neural network (CNN) models have been shown to produce light response predictions that were substantially more accurate than those of a LN model. However, this modeling approach has not yet been applied to responses of macaque or human RGCs to natural images. Here, we train and test a CNN model on responses to natural images of the four numerically dominant RGC types in the macaque and human retina - ON parasol, OFF parasol, ON midget and OFF midget cells. Compared with the LN model, the CNN model provided substantially more accurate response predictions. Linear reconstructions of the visual stimulus were more accurate for CNN compared to LN model-generated responses, relative to reconstructions obtained from the recorded data. These findings demonstrate the effectiveness of a CNN model in capturing light responses of major RGC types in the macaque and human retinas in natural conditions.

neuroscience↗

bioRxiv2023

Decomposition of retinal ganglion cell electrical images for cell type and functional inference

ObjectiveIdentifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. ApproachThe electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. ResultsThe decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches. SignificanceThese findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.

neuroscience↗

bioRxiv2023

Fixational Eye Movements Enhance the Precision of Visual Information Transmitted by the Primate Retina

Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulusevoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

neuroscience↗

bioRxiv2022

Precise control of neural activity using temporally dithered and spatially multiplexed electrical stimulation

Neural implants have the potential to restore lost sensory function by electrically evoking the complex naturalistic activity patterns of neural populations. However, it can be difficult to predict and control evoked neural responses to simultaneous multi-electrode stimulation due to nonlinearity of the responses. We present a solution to this problem and demonstrate its utility in the context of a bi-directional retinal implant for restoring vision. A dynamically optimized stimulation approach encodes incoming visual stimuli into a rapid, greedily chosen, temporally dithered and spatially multiplexed sequence of simple stimulation patterns. Stimuli are selected to optimize the reconstruction of the visual stimulus from the evoked responses. Temporal dithering exploits the slow time scales of downstream neural processing, and spatial multiplexing exploits the independence of responses generated by distant electrodes. The approach was evaluated using an experimental laboratory prototype of a retinal implant: large-scale, high-resolution multi-electrode stimulation and recording of macaque and rat retinal ganglion cells ex vivo. The dynamically optimized stimulation approach substantially enhanced performance compared to existing approaches based on static mapping between visual stimulus intensity and current amplitude. The modular framework enabled parallel extensions to naturalistic viewing conditions, incorporation of perceptual similarity measures, and efficient implementation for an implantable device. A direct closed-loop test of the approach supported its potential use in vision restoration. Impact StatementA novel method of electrical stimulation to precisely control neural activity for sensory restoration exhibits improvements in visual stimulus reconstruction, enables efficient hardware design, and extends to naturalistic conditions.

neuroscience↗

bioRxiv2022

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

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.

neuroscience↗

bioRxiv2022

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

Electrical stimulation of retinal ganglion cells (RGCs) with electronic implants provides rudimentary artificial vision to people blinded by retinal degeneration. However, current devices stimulate indiscriminately and therefore cannot reproduce the intricate neural code of the retina. Recent work has demonstrated more precise activation of RGCs using focal electrical stimulation with multi-electrode arrays in the peripheral macaque retina, but it is unclear how effective this can be in the central retina, which is required for high-resolution vision. This work probes the neural code and effectiveness of focal epiretinal stimulation in the central macaque retina, using large-scale electrical recording and stimulation ex vivo. The functional organization, light response properties, and electrical properties of the major RGC types in the central retina were mostly similar to the peripheral retina, with some notable differences in density, kinetics, linearity, spiking statistics and correlations. The major RGC types could be distinguished by their intrinsic electrical properties. Electrical stimulation targeting parasol cells revealed similar activation thresholds and reduced axon bundle activation in the central retina, but lower stimulation selectivity. Quantitative evaluation of the potential for image reconstruction from electrically-evoked parasol cell signals revealed higher overall expected image quality in the central retina. An exploration of inadvertent midget cell activation suggested that it could contribute high spatial frequency noise to the visual signal carried by parasol cells. These results support the possibility of reproducing high-acuity visual signals in the central retina with an epiretinal implant. Significance StatementArtificial restoration of vision with retinal implants is a major treatment for blindness. However, present-day implants do not provide high-resolution visual perception, in part because they do not reproduce the natural neural code of the retina. Here we demonstrate the level of visual signal reproduction that is possible with a future implant by examining how accurately responses to electrical stimulation of parasol retinal ganglion cells (RGCs) can convey visual signals. Although the precision of electrical stimulation in the central retina was diminished relative to the peripheral retina, the quality of expected visual signal reconstruction in parasol cells was greater. These findings suggest that visual signals could be restored with high fidelity in the central retina using a future retinal implant.

neuroscience↗

bioRxiv2021

Spatially Patterned Bi-electrode Epiretinal Stimulation for Axon Avoidance at Cellular Resolution

ObjectiveEpiretinal prostheses are designed to restore vision to people blinded by photoreceptor degenerative diseases by stimulating surviving retinal ganglion cells (RGCs), which carry visual signals to the brain. However, inadvertent stimulation of RGCs at their axons can result in non-focal visual percepts, limiting the quality of artificial vision. Theoretical work has suggested that axon activation can be avoided with current stimulation designed to minimize the second spatial derivative of the induced extracellular voltage along the axon. However, this approach has not been verified experimentally at the resolution of single cells. Approach. In this work, a custom multi-electrode array (512 electrodes, 10 m diameter, 60 m pitch) was used to stimulate and record RGCs in macaque retina ex vivo at single-cell, single-spike resolution. RGC activation thresholds resulting from bi-electrode stimulation, which consisted of bipolar currents simultaneously delivered through two electrodes straddling an axon, were compared to activation thresholds from traditional single-electrode stimulation. ResultsOn average, across three retinal preparations, the bi-electrode stimulation strategy reduced somatic activation thresholds ([~]21%) while increasing axonal activation thresholds ([~]14%), thus favoring selective somatic activation. Furthermore, individual examples revealed rescued selective activation of somas that was not possible with any individual electrode. Significance. This work suggests that a bi-electrode epiretinal stimulation strategy can reduce inadvertent axonal activation at cellular resolution, for high-fidelity artificial vision. Novelty & SignificanceThe effectiveness of bi-electrode stimulation for enhancing the electrical activation of retinal neurons was tested using high-density multi-electrode recording and stimulation in isolated macaque retina. The results suggest that spatially patterned bi-electrode stimuli reduce unwanted axon activation and thus improve the selectivity of stimulation at cellular resolution. Similar patterns could be implemented in a future high-resolution prosthesis to permit a more faithful replication of normal retinal activity, at the resolution of single-cells and single-spikes, for the treatment of incurable blindness.

bioengineering↗

bioRxiv2020

Focal Electrical Stimulation of Human Retinal Ganglion Cells

Vision restoration with retinal implants that electrically stimulate retinal ganglion cells (RGCs), which transmit visual information to the brain, is limited by indiscriminate activation of many cells and cell types. Recent work in isolated macaque retina has demonstrated that direct electrical stimulation of RGCs can be performed with single-cell, single-spike resolution. However, the fidelity of epiretinal stimulation has not been examined in the human retina. Here, electrical activation of the major RGC types was examined using large-scale, multi-electrode recording and stimulation in the human retina ex vivo and compared directly to results from macaque. Targeted activation with single-cell, single-spike resolution was often possible without activating overlying axon bundles, at low stimulation current levels similar to those in macaque. Distinct cell types could be identified and targeted based on their distinct electrical signatures. Simulation based on these measurements revealed that a novel, dynamic stimulation approach would produce a nearly optimal evoked visual signal. These results indicate that high-fidelity control of spiking in human RGCs is achievable with extracellular stimulation and that the macaque retina is an accurate model for vision restoration with epiretinal implants.

neuroscience↗

bioRxiv2020

Functional Organization of Midget and Parasol Ganglion Cells in the Human Retina

The functional organization of diverse retinal ganglion cell (RGC) types, which shapes the visual signal transmitted to the brain, has been examined in many species. The unique spatial, temporal, and chromatic properties of the numerically dominant RGC types in macaque monkey retina are presumed to most accurately model human vision. However, the functional similarity between RGCs in macaques and humans has only begun to be tested, and recent work suggests possible differences. Here, the properties of the numerically dominant human RGC types were examined using large-scale multi-electrode recordings with fine-grained visual stimulation in isolated retina, and compared to results from dozens of recordings from macaque retina using the same experimental methods and conditions. The properties of four major human RGC types -- ON-parasol, OFF-parasol, ON-midget, and OFF-midget -- closely paralleled those of the same macaque RGC types, including the spatial and temporal light sensitivity, precisely coordinated mosaic organization of receptive fields, ON-OFF asymmetries, spatial response nonlinearity, and sampling of photoreceptor inputs over space. Putative smooth monostratified cells and polyaxonal amacrine cells were also identified based on similarities to cell types previously identified in macaque retina. The results suggest that recently proposed differences between human and macaque RGCs probably reflect experimental differences, and that the macaque model provides an accurate picture of human RGC function.

neuroscience↗

bioRxiv2019

Tet2 negatively regulates memory fidelity

Despite being fully differentiated, DNA methylation is dynamically regulated in post-mitotic glutamatergic neurons in the CA1 of the hippocampus through competing active DNA methylation and de-methylation, a process that regulates neuronal plasticity. Active DNA methylation after learning is necessary for long-term memory formation, and active DNA de-methylation by the TET enzymes has been implicated as a counter-regulator of that biochemical process. We demonstrate that Tet2 functions in the CA1 as a negative regulator of long-term memory, whereby its knockdown across the CA1 or haploinsufficiency in glutamatergic neurons enhances the fidelity of hippocampal-dependent spatial and associative memory. Loci of altered DNA methylation were then determined using whole genome bisulfite sequencing from glutamatergic Tet2 haploinsufficient CA1 tissue, which revealed hypermethylation in the promoters of genes known to be transcriptionally regulated after experiential learning. This study demonstrates a link between Tet2 activity at genes important for memory formation in CA1 glutamatergic neurons and memory fidelity.

neuroscience↗