Alexandra Kling

All Publications

• Direct-Print 3D Electrodes for Large-Scale, High-Density, and Customizable Neural Interfaces. Advanced science (Weinheim, Baden-Wurttemberg, Germany) Wang, P., Wu, E. G., Uluşan, H., Zhao, E. T., Phillips, A. J., Kling, A., Hays, M. R., Vasireddy, P. K., Madugula, S., Vilkhu, R., Hierlemann, A., Hong, G., Chichilnisky, E. J., Melosh, N. A. 2024: e2408602

  Abstract

  Silicon-based microelectronics can scalably record and modulate neural activity at high spatiotemporal resolution, but their planar form factor poses challenges in targeting 3D neural structures. A method for fabricating tissue-penetrating 3D microelectrodes directly onto planar microelectronics using high-resolution 3D printing via 2-photon polymerization and scalable microfabrication technologies are presented. This approach enables customizable electrode shape, height, and positioning for precise targeting of neuron populations distributed in 3D. The effectiveness of this approach is demonstrated in tackling the critical challenge of interfacing with the retina-specifically, selectively targeting retinal ganglion cell (RGC) somas while avoiding the axon bundle layer. 6,600-microelectrode, 35 µm pitch, tissue-penetrating arrays are fabricated to obtain high-fidelity, high-resolution, and large-scale retinal recording that reveals little axonal interference, a capability previously undemonstrated. Confocal microscopy further confirms the precise placement of the microelectrodes. This technology can be a versatile solution for interfacing silicon microelectronics with neural structures at a large scale and cellular resolution.

  View details for DOI 10.1002/advs.202408602

  View details for PubMedID 39588825

• Fixational eye movements enhance the precision of visual information transmitted by the primate retina. Nature communications Wu, E. G., Brackbill, N., Rhoades, C., Kling, A., Gogliettino, A. R., Shah, N. P., Sher, A., Litke, A. M., Simoncelli, E. P., Chichilnisky, E. J. 2024; 15 (1): 7964

  Abstract

  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 stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

  View details for DOI 10.1038/s41467-024-52304-7

  View details for PubMedID 39261491

  View details for PubMedCentralID PMC11390888

• Decomposition of retinal ganglion cell electrical images for cell type and functional inference. bioRxiv : the preprint server for biology Wu, E. G., Rudzite, A. M., Bohlen, M. O., Li, P. H., Kling, A., Cooler, S., Rhoades, C., Brackbill, N., Gogliettino, A. R., Shah, N. P., Madugula, S. S., Sher, A., Litke, A. M., Field, G. D., Chichilnisky, E. J. 2023

  Abstract

  Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and 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. The 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. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. 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), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.

  View details for DOI 10.1101/2023.11.06.565889

  View details for PubMedID 37986895

  View details for PubMedCentralID PMC10659265

• Fixational Eye Movements Enhance the Precision of Visual Information Transmitted by the Primate Retina. bioRxiv : the preprint server for biology Wu, E. G., Brackbill, N., Rhoades, C., Kling, A., Gogliettino, A. R., Shah, N. P., Sher, A., Litke, A. M., Simoncelli, E. P., Chichilnisky, E. J. 2023

  Abstract

  The retina transmits visual signals to the brain in the spiking activity of retinal ganglion cells (RGCs). This signal is necessarily imperfect: some visual information is lost in phototransduction and retinal processing. To quantify the transmitted visual signal, we developed a Bayesian method to reconstruct images from the simultaneously recorded spikes of hundreds of macaque RGCs of the four dominant types. The algorithm combines a stochastic likelihood model for RGC light responses that is fitted to spiking data, with a prior model for natural images implicitly embedded within an artificial neural network trained for image denoising. When applied to retinal population responses to both flashed images and images jittered to emulate fixational eye movements, the method provided reconstruction performance exceeding or matching all previous reconstruction algorithms, in an interpretable analytical framework that provided insight into the neural code. Reconstructions improved with increasing jitter amplitude over a behaviorally relevant range (even when the jitter trajectory was unknown), revealing that fixational eye movements improve rather than degrade the retinal signal. Reconstructions were degraded by artificial perturbation of spike times as small as 5 ms, revealing a temporal encoding precision finer than expected from previous studies. Ablating cell-to-cell interactions in the encoding model substantially reduced reconstruction quality, indicating the importance of stimulus-evoked correlations in representing the visual scene. Thus, fixational eye movements contribute to highly precise retinal population activity, enabling more accurate transmission of visual signals to the brain.

  View details for DOI 10.1101/2023.08.12.552902

  View details for PubMedID 37645934

• Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures. Journal of neural engineering Zaidi, M., Aggarwal, G., Shah, N. P., Karniol-Tambour, O., Goetz, G., Madugula, S. S., Gogliettino, A., Wu, E. G., Kling, A., Brackbill, N., Sher, A., Litke, A. M., Chichilnisky, E. J. 2023

  Abstract

  Objective Retinal implants are designed to stimulate retinal ganglion cells (RGCs) in a way that restores sight to individuals blinded by photoreceptor degeneration. Reproducing high-acuity vision with these devices will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without being able to measure them directly. Here we demonstrate an inference approach that exploits intrinsic electrophysiological features of primate RGCs. Approach First, ON-parasol and OFF-parasol RGC types were identified using their intrinsic electrical features in large-scale multi-electrode recordings from macaque retina. Then, the electrically inferred somatic location, inferred cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. The accuracy of the cell type classification and of reproducing measured light responses with the model were evaluated. Main results A cell-type classifier trained on 246 large-scale multi-electrode recordings from 148 retinas achieved 95% mean accuracy on 29 test retinas. In five retinas tested, the inferred 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). Linear decoding of natural images from predicted RGC activity in one retina showed a mean correlation of 0.55 between decoded and true images, compared to an upper bound of 0.81 using models fitted to light response data. Significance These results suggest that inference of RGC light response properties from intrinsic features of their electrical activity may be a useful approach for high-fidelity sight restoration. The overall strategy of first inferring cell type from electrical features and then exploiting cell type to help infer natural cell function may also prove broadly useful to neural interfaces.

  View details for DOI 10.1088/1741-2552/ace657

  View details for PubMedID 37433293

• Direct-print three-dimensional electrodes for large- scale, high-density, and customizable neural inter- faces. bioRxiv : the preprint server for biology Wang, P., Wu, E. G., Uluşan, H., Phillips, A. J., Rose Hays, M., Kling, A., Zhao, E. T., Madugula, S., Vilkhu, R. S., Vasireddy, P. K., Hier-Lemann, A., Hong, G., Chichilnisky, E. J., Melosh, N. A. 2023

  Abstract

  Silicon-based planar microelectronics is a powerful tool for scalably recording and modulating neural activity at high spatiotemporal resolution, but it remains challenging to target neural structures in three dimensions (3D). We present a method for directly fabricating 3D arrays of tissue-penetrating microelectrodes onto silicon microelectronics. Leveraging a high-resolution 3D printing technology based on 2-photon polymerization and scalable microfabrication processes, we fabricated arrays of 6,600 microelectrodes 10-130 μm tall and at 35-μm pitch onto a planar silicon-based microelectrode array. The process enables customizable electrode shape, height and positioning for precise targeting of neuron populations distributed in 3D. As a proof of concept, we addressed the challenge of specifically targeting retinal ganglion cell (RGC) somas when interfacing with the retina. The array was customized for insertion into the retina and recording from somas while avoiding the axon layer. We verified locations of the microelectrodes with confocal microscopy and recorded high-resolution spontaneous RGC activity at cellular resolution. This revealed strong somatic and dendritic components with little axon contribution, unlike recordings with planar microelectrode arrays. The technology could be a versatile solution for interfacing silicon microelectronics with neural structures and modulating neural activity at large scale with single-cell resolution.

  View details for DOI 10.1101/2023.05.30.542925

  View details for PubMedID 37398164

  View details for PubMedCentralID PMC10312573

• Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells. The Journal of neuroscience : the official journal of the Society for Neuroscience Madugula, S. S., Vilkhu, R., Shah, N. P., Grosberg, L. E., Kling, A., Gogliettino, A. R., Nguyen, H., Hottowy, P., Sher, A., Litke, A. M., Chichilnisky, E. J. 2023

  Abstract

  High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, in order to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multi-electrode stimulation and recording from male and female macaque retinal ganglion cells (RGCs) ex vivo Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly consistent. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants.Significance Statement:This study demonstrates that individual in situ primate retinal ganglion cells of different types respond to artificially generated, external electrical fields in a systematic manner, in accordance with theoretical predictions, that allows for prediction of electrical stimulus sensitivity from recorded spontaneous activity. It also provides evidence that such an approach could be immensely helpful in the calibration of clinical retinal implants.

  View details for DOI 10.1523/JNEUROSCI.1023-22.2023

  View details for PubMedID 37268418

• Joint physiological and connectomic analysis of neural circuitry in the primate retina Aji, I., Kling, A., Haverkamp, S., Watkins, P., Chichilnisky, E. J., Briggman, K. L. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2023

  View details for Web of Science ID 001053758304266

• Morphological identification of novel functional ganglion and amacrine cell types in macaque retina Kling, A., Manookin, M. B., Rieke, F., Cooler, S., Sher, A., Litke, A., Chichilnisky, E. J. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2023

  View details for Web of Science ID 001053758300129

• High-fidelity reproduction of visual signals by electrical stimulation in the central primate retina. The Journal of neuroscience : the official journal of the Society for Neuroscience Gogliettino, A. R., Madugula, S. S., Grosberg, L. E., Vilkhu, R. S., Brown, J., Nguyen, H., Kling, A., Hottowy, P., Dąbrowski, W., Sher, A., Litke, A. M., Chichilnisky, E. J. 2023

  Abstract

  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 STATEMENT:Artificial 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.

  View details for DOI 10.1523/JNEUROSCI.1091-22.2023

  View details for PubMedID 37188516

• Focal electrical stimulation of human retinal ganglion cells for vision restoration. Journal of neural engineering Madugula, S. S., Gogliettino, A. R., Zaidi, M., Aggarwal, G., Kling, A., Shah, N. P., Brown, J. B., Vilkhu, R., Hays, M. R., Nguyen, H., Fan, V., Wu, E. G., Hottowy, P., Sher, A., Litke, A. M., Silva, R. A., Chichilnisky, E. J. 2022; 19 (6)

  Abstract

  Objective. Vision restoration with retinal implants is limited by indiscriminate simultaneous activation of many cells and cell types, which is incompatible with reproducing the neural code of the retina. Recent work has shown that primate retinal ganglion cells (RGCs), which transmit visual information to the brain, can be directly electrically activated with single-cell, single-spike, cell-type precision - however, this possibility has never been tested in the human retina. In this study we aim to characterize, for the first time, direct in situ extracellular electrical stimulation of individual human RGCs.Approach. Extracellular electrical stimulation of individual human RGCs was conducted in three human retinas ex vivo using a custom large-scale, multi-electrode array capable of simultaneous recording and stimulation. Measured activation properties were compared directly to extensive results from macaque.Main results. Precise activation was in many cases possible without activating overlying axon bundles, at low stimulation current levels similar to those used in macaque. The major RGC types could be identified and targeted based on their distinctive electrical signatures. The measured electrical activation properties of RGCs, combined with a dynamic stimulation algorithm, was sufficient to produce an evoked visual signal that was nearly optimal given the constraints of the interface.Significance. These results suggest the possibility of high-fidelity vision restoration in humans using bi-directional epiretinal implants.

  View details for DOI 10.1088/1741-2552/aca5b5

  View details for PubMedID 36533865

• Suppression without inhibition: how retinal computation contributes to saccadic suppression. Communications biology Idrees, S., Baumann, M., Korympidou, M. M., Schubert, T., Kling, A., Franke, K., Hafed, Z. M., Franke, F., Munch, T. A. 2022; 5 (1): 692

  Abstract

  Visual perception remains stable across saccadic eye movements, despite the concurrent strongly disruptive visual flow. This stability is partially associated with a reduction in visual sensitivity, known as saccadic suppression, which already starts in the retina with reduced ganglion cell sensitivity. However, the retinal circuit mechanisms giving rise to such suppression remain unknown. Here, we describe these mechanisms using electrophysiology in mouse, pig, and macaque retina, 2-photon calcium imaging, computational modeling, and human psychophysics. We find that sequential stimuli, like those that naturally occur during saccades, trigger three independent suppressive mechanisms in the retina. The main mechanism is triggered by contrast-reversing sequential stimuli and originates within the receptive field center of ganglion cells. It does not involve inhibition or other known suppressive mechanisms like saturation or adaptation. Instead, it relies on temporal filtering of the inherently slow response of cone photoreceptors coupled with downstream nonlinearities. Two further mechanisms of suppression are present predominantly in ON ganglion cells and originate in the receptive field surround, highlighting another disparity between ON and OFF ganglion cells. The mechanisms uncovered here likely play a role in shaping the retinal output following eye movements and other natural viewing conditions where sequential stimulation is ubiquitous.

  View details for DOI 10.1038/s42003-022-03526-2

  View details for PubMedID 35821404

• Automated identification of ganglion and amacrine cell types in a large primate retina dataset Cooler, S., Kling, A., Wu, E., Rhoades, C., Brackbill, N., Sher, A., Litke, A., Chichilnisky, E. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2022

  View details for Web of Science ID 000844437007005

• Unusual properties of novel ganglion cell and amacrine cell types in macaque and human retina Kling, A., Wu, E., Cooler, S., Rhoades, C., Brackbill, N., Litke, A., Sher, A., Chichilnisky, E. J. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2022

  View details for Web of Science ID 000844401303148

• Individual variability of neural computations in the primate retina. Neuron Shah, N. P., Brackbill, N., Samarakoon, R., Rhoades, C., Kling, A., Sher, A., Litke, A., Singer, Y., Shlens, J., Chichilnisky, E. J. 1800

  Abstract

  Variation in the neural code contributes to making each individual unique. We probed neural code variation using 100 population recordings from major ganglion cell types in the macaque retina, combined with an interpretable computational representation of individual variability. This representation captured variation and covariation in properties such as nonlinearity, temporal dynamics, and spatial receptive field size and preserved invariances such as asymmetries between On and Off cells. The covariation of response properties in different cell types was associated with the proximity of lamination of their synaptic input. Surprisingly, male retinas exhibited higher firing rates and faster temporal integration than female retinas. Exploiting data from previously recorded retinas enabled efficient characterization of a new macaque retina, and of a human retina. Simulations indicated that combining a large dataset of retinal recordings with behavioral feedback could reveal the neural code in a living human and thus improve vision restoration with retinal implants.

  View details for DOI 10.1016/j.neuron.2021.11.026

  View details for PubMedID 34932942

• Inferring retinal ganglion cell light response properties from intrinsic electrical feature Zaidi, M., Aggarwal, G., Shah, N. P., Karniol-Tambour, O., Goetz, G., Madugula, S., Gogliettino, A. R., Wu, E. G., Kling, A., Brackbill, N., Sher, A., Litke, A. M., Chichilnisky, E. J. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2021

  View details for Web of Science ID 000690761600407

• Inference of nonlinear receptive field subunits with spike-triggered clustering. eLife Shah, N. P., Brackbill, N., Rhoades, C., Kling, A., Goetz, G., Litke, A. M., Sher, A., Simoncelli, E., Chichilnisky, E. J. 2020; 9

  Abstract

  Responses of sensory neurons are often modeled using a weighted combination of rectified linear subunits. Since these subunits often cannot be measured directly, a flexible method is needed to infer their properties from the responses of downstream neurons. We present a method for maximum likelihood estimation of subunits by soft-clustering spike-triggered stimuli, and demonstrate its effectiveness in visual neurons. For parasol retinal ganglion cells in macaque retina, estimated subunits partitioned the receptive field into compact regions, likely representing aggregated bipolar cell inputs. Joint clustering revealed shared subunits between neighboring cells, producing a parsimonious population model. Closed-loop validation, using stimuli lying in the null space of the linear receptive field, revealed stronger nonlinearities in OFF cells than ON cells. Responses to natural images, jittered to emulate fixational eye movements, were accurately predicted by the subunit model. Finally, the generality of the approach was demonstrated in macaque V1 neurons.

  View details for DOI 10.7554/eLife.45743

  View details for PubMedID 32149600

• Reconstruction of natural images from responses of primate retinal ganglion cells. eLife Brackbill, N. n., Rhoades, C. n., Kling, A. n., Shah, N. P., Sher, A. n., Litke, A. M., Chichilnisky, E. J. 2020; 9

  Abstract

  The visual message conveyed by a retinal ganglion cell (RGC) is often summarized by its spatial receptive field, but in principle also depends on the responses of other RGCs and natural image statistics. This possibility was explored by linear reconstruction of natural images from responses of the four numerically-dominant macaque RGC types. Reconstructions were highly consistent across retinas. The optimal reconstruction filter for each RGC - its visual message - reflected natural image statistics, and resembled the receptive field only when nearby, same-type cells were included. ON and OFF cells conveyed largely independent, complementary representations, and parasol and midget cells conveyed distinct features. Correlated activity and nonlinearities had statistically significant but minor effects on reconstruction. Simulated reconstructions, using linear-nonlinear cascade models of RGC light responses that incorporated measured spatial properties and nonlinearities, produced similar results. Spatiotemporal reconstructions exhibited similar spatial properties, suggesting that the results are relevant for natural vision.

  View details for DOI 10.7554/eLife.58516

  View details for PubMedID 33146609

• Unusual Physiological Properties of Smooth Monostratified Ganglion Cell Types in Primate Retina. Neuron Rhoades, C. E., Shah, N. P., Manookin, M. B., Brackbill, N., Kling, A., Goetz, G., Sher, A., Litke, A. M., Chichilnisky, E. J. 2019

  Abstract

  The functions of the diverse retinal ganglion cell types in primates and the parallel visual pathways they initiate remain poorly understood. Here, unusual physiological and computational properties of the ON and OFF smooth monostratified ganglion cells are explored. Large-scale multi-electrode recordings from 48 macaque retinas revealed that these cells exhibit irregular receptive field structure composed of spatially segregated hotspots, quite different from the classic center-surround model of retinal receptive fields. Surprisingly, visual stimulation of different hotspots in the same cell produced spikes with subtly different spatiotemporal voltage signatures, consistent with a dendritic contribution to hotspot structure. Targeted visual stimulation and computational inference demonstrated strong nonlinear subunit properties associated with each hotspot, supporting a model in which the hotspots apply nonlinearities at a larger spatial scale than bipolar cells. These findings reveal a previously unreported nonlinear mechanism in the output of the primate retina that contributes to signaling spatial information.

  View details for DOI 10.1016/j.neuron.2019.05.036

  View details for PubMedID 31227309