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Wu EG, Rudzite AM, Bohlen MO, Li PH, Kling A, Cooler S, Rhoades C, Brackbill N, Gogliettino AR, Shah NP, Madugula SS, Sher A, Litke AM, Field GD, Chichilnisky E. Decomposition of retinal ganglion cell electrical images for cell type and functional inference. bioRxiv 2023:2023.11.06.565889. [PMID: 37986895 PMCID: PMC10659265 DOI: 10.1101/2023.11.06.565889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
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.
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Affiliation(s)
- Eric G. Wu
- Department of Electrical Engineering, Stanford University
| | | | | | - Peter H. Li
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
| | - Alexandra Kling
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
| | - Sam Cooler
- Department of Neurosurgery, Stanford University
| | | | | | | | - Nishal P. Shah
- Department of Electrical Engineering, Stanford University
- Department of Neurosurgery, Stanford University
| | - Sasidhar S. Madugula
- Neurosciences PhD Program, Stanford University
- Stanford School of Medicine, Stanford University
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz
| | - Alan M. Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz
| | - Greg D. Field
- Department of Neurobiology, Duke University
- Stein Eye Institute, Department of Ophthalmology, University of California, Los Angeles
| | - E.J. Chichilnisky
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
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2
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Wu EG, Brackbill N, Rhoades C, Kling A, Gogliettino AR, Shah NP, Sher A, Litke AM, Simoncelli EP, Chichilnisky E. Fixational Eye Movements Enhance the Precision of Visual Information Transmitted by the Primate Retina. bioRxiv 2023:2023.08.12.552902. [PMID: 37645934 PMCID: PMC10462030 DOI: 10.1101/2023.08.12.552902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
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 visual signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of macaque retinal ganglion cells (RGCs) of the major cell types, 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 provided an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the jitter trajectory was 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.
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Affiliation(s)
- Eric G. Wu
- Department of Electrical Engineering, Stanford University
| | | | | | - Alexandra Kling
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
| | - Alex R. Gogliettino
- Hansen Experimental Physics Laboratory, Stanford University
- Neurosciences PhD Program, Stanford University
| | - Nishal P. Shah
- Department of Electrical Engineering, Stanford University
- Department of Neurosurgery, Stanford University
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz
| | - Alan M. Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz
| | - Eero P. Simoncelli
- Flatiron Institute, Simons Foundation
- Center for Neural Science, New York University
- Courant Institute of Mathematical Sciences, New York University
| | - E.J. Chichilnisky
- Department of Neurosurgery, Stanford University
- Department of Ophthalmology, Stanford University
- Hansen Experimental Physics Laboratory, Stanford University
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3
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Zaidi M, Aggarwal G, Shah NP, Karniol-Tambour O, Goetz G, Madugula SS, Gogliettino AR, Wu EG, Kling A, Brackbill N, Sher A, Litke AM, Chichilnisky EJ. Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures. J Neural Eng 2023; 20:10.1088/1741-2552/ace657. [PMID: 37433293 PMCID: PMC11067857 DOI: 10.1088/1741-2552/ace657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/11/2023] [Indexed: 07/13/2023]
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.
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Affiliation(s)
- Moosa Zaidi
- Stanford University School of Medicine, Stanford University, Stanford, CA, United States of America
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Gorish Aggarwal
- Neurosurgery, Stanford University, Stanford, CA, United States of America
- Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Nishal P Shah
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Orren Karniol-Tambour
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States of America
| | - Georges Goetz
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Sasidhar S Madugula
- Stanford University School of Medicine, Stanford University, Stanford, CA, United States of America
- Neurosciences, Stanford University, Stanford, CA, United States of America
| | - Alex R Gogliettino
- Neurosciences, Stanford University, Stanford, CA, United States of America
| | - Eric G Wu
- Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Alexandra Kling
- Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Nora Brackbill
- Physics, Stanford University, Stanford, CA, United States of America
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, United States of America
| | - E J Chichilnisky
- Neurosurgery, Stanford University, Stanford, CA, United States of America
- Ophthalmology, Stanford University, Stanford, CA, United States of America
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Shah NP, Brackbill N, Samarakoon R, Rhoades C, Kling A, Sher A, Litke A, Singer Y, Shlens J, Chichilnisky EJ. Individual variability of neural computations in the primate retina. Neuron 2022; 110:698-708.e5. [PMID: 34932942 PMCID: PMC8857061 DOI: 10.1016/j.neuron.2021.11.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/10/2021] [Accepted: 11/20/2021] [Indexed: 12/28/2022]
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.
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Affiliation(s)
- Nishal P Shah
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA; Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
| | - Nora Brackbill
- Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA
| | - Ryan Samarakoon
- Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Colleen Rhoades
- Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Alexandra Kling
- Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
| | - Alexander Sher
- University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alan Litke
- University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Yoram Singer
- WorldQuant, LLC, 1700 E Putnam Ave., Old Greenwich, CT 06870, USA
| | - Jonathon Shlens
- Google Brain, 1600 Amphitheatre Pkwy., Mountain View, CA 94043, USA
| | - E J Chichilnisky
- Hansen Experimental Physics Lab, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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5
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Kim YJ, Brackbill N, Batty E, Lee J, Mitelut C, Tong W, Chichilnisky EJ, Paninski L. Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Comput 2021; 33:1719-1750. [PMID: 34411268 DOI: 10.1162/neco_a_01395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 01/25/2021] [Indexed: 11/04/2022]
Abstract
Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.
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Brackbill N, Rhoades C, Kling A, Shah NP, Sher A, Litke AM, Chichilnisky EJ. Reconstruction of natural images from responses of primate retinal ganglion cells. eLife 2020; 9:e58516. [PMID: 33146609 PMCID: PMC7752138 DOI: 10.7554/elife.58516] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 11/02/2020] [Indexed: 11/23/2022] Open
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.
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Affiliation(s)
- Nora Brackbill
- Department of Physics, Stanford UniversityStanfordUnited States
| | - Colleen Rhoades
- Department of Bioengineering, Stanford UniversityStanfordUnited States
| | - Alexandra Kling
- Department of Neurosurgery, Stanford School of MedicineStanfordUnited States
- Department of Ophthalmology, Stanford UniversityStanfordUnited States
- Hansen Experimental Physics Laboratory, Stanford UniversityStanfordUnited States
| | - Nishal P Shah
- Department of Electrical Engineering, Stanford UniversityStanfordUnited States
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa CruzSanta CruzUnited States
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa CruzSanta CruzUnited States
| | - EJ Chichilnisky
- Department of Neurosurgery, Stanford School of MedicineStanfordUnited States
- Department of Ophthalmology, Stanford UniversityStanfordUnited States
- Hansen Experimental Physics Laboratory, Stanford UniversityStanfordUnited States
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7
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Shah NP, Brackbill N, Rhoades C, Kling A, Goetz G, Litke AM, Sher A, Simoncelli EP, Chichilnisky EJ. Inference of nonlinear receptive field subunits with spike-triggered clustering. eLife 2020; 9:e45743. [PMID: 32149600 PMCID: PMC7062463 DOI: 10.7554/elife.45743] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 10/29/2019] [Indexed: 11/25/2022] Open
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.
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Affiliation(s)
- Nishal P Shah
- Department of Electrical EngineeringStanford UniversityStanfordUnited States
| | - Nora Brackbill
- Department of PhysicsStanford UniversityStanfordUnited States
| | - Colleen Rhoades
- Department of BioengineeringStanford UniversityStanfordUnited States
| | - Alexandra Kling
- Department of NeurosurgeryStanford School of MedicineStanfordUnited States
- Department of OphthalmologyStanford UniversityStanfordUnited States
- Hansen Experimental Physics LaboratoryStanford UniversityStanfordUnited States
| | - Georges Goetz
- Department of NeurosurgeryStanford School of MedicineStanfordUnited States
- Department of OphthalmologyStanford UniversityStanfordUnited States
- Hansen Experimental Physics LaboratoryStanford UniversityStanfordUnited States
| | - Alan M Litke
- Institute for Particle PhysicsUniversity of California, Santa CruzSanta CruzUnited States
| | - Alexander Sher
- Santa Cruz Institute for Particle PhysicsUniversity of California, Santa CruzSanta CruzUnited States
| | - Eero P Simoncelli
- Center for Neural ScienceNew York UniversityNew YorkUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - EJ Chichilnisky
- Department of NeurosurgeryStanford School of MedicineStanfordUnited States
- Department of OphthalmologyStanford UniversityStanfordUnited States
- Hansen Experimental Physics LaboratoryStanford UniversityStanfordUnited States
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8
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Obaid A, Hanna ME, Wu YW, Kollo M, Racz R, Angle MR, Müller J, Brackbill N, Wray W, Franke F, Chichilnisky EJ, Hierlemann A, Ding JB, Schaefer AT, Melosh NA. Massively parallel microwire arrays integrated with CMOS chips for neural recording. Sci Adv 2020; 6:eaay2789. [PMID: 32219158 PMCID: PMC7083623 DOI: 10.1126/sciadv.aay2789] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 12/26/2019] [Indexed: 05/21/2023]
Abstract
Multi-channel electrical recordings of neural activity in the brain is an increasingly powerful method revealing new aspects of neural communication, computation, and prosthetics. However, while planar silicon-based CMOS devices in conventional electronics scale rapidly, neural interface devices have not kept pace. Here, we present a new strategy to interface silicon-based chips with three-dimensional microwire arrays, providing the link between rapidly-developing electronics and high density neural interfaces. The system consists of a bundle of microwires mated to large-scale microelectrode arrays, such as camera chips. This system has excellent recording performance, demonstrated via single unit and local-field potential recordings in isolated retina and in the motor cortex or striatum of awake moving mice. The modular design enables a variety of microwire types and sizes to be integrated with different types of pixel arrays, connecting the rapid progress of commercial multiplexing, digitisation and data acquisition hardware together with a three-dimensional neural interface.
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Affiliation(s)
- Abdulmalik Obaid
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Mina-Elraheb Hanna
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
- Paradromics Inc., Austin, TX, USA
| | - Yu-Wei Wu
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Mihaly Kollo
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Romeo Racz
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, UK
| | | | - Jan Müller
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Nora Brackbill
- Department of Physics, Stanford University, Stanford, CA, USA
| | - William Wray
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, UK
| | - Felix Franke
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - E. J. Chichilnisky
- Departments of Neurosurgery and Ophthalmology, Stanford University, Stanford, CA, USA
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Jun B. Ding
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Andreas T. Schaefer
- Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, UK
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Nicholas A. Melosh
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
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9
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Rhoades CE, Shah NP, Manookin MB, Brackbill N, Kling A, Goetz G, Sher A, Litke AM, Chichilnisky EJ. Unusual Physiological Properties of Smooth Monostratified Ganglion Cell Types in Primate Retina. Neuron 2019; 103:658-672.e6. [PMID: 31227309 PMCID: PMC6817368 DOI: 10.1016/j.neuron.2019.05.036] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/26/2019] [Accepted: 05/22/2019] [Indexed: 02/06/2023]
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.
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Affiliation(s)
- Colleen E Rhoades
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
| | - Nishal P Shah
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Michael B Manookin
- Department of Ophthalmology, University of Washington, Seattle, WA 98195, USA
| | - Nora Brackbill
- Department of Physics, Stanford University, Stanford, CA 94305, USA
| | - Alexandra Kling
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Georges Goetz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, USA
| | - Alexander Sher
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Alan M Litke
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - E J Chichilnisky
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; Department of Ophthalmology Stanford University, Stanford, CA 94305, USA; Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA 94305, USA
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10
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Kelly A, Brackbill N, Markland TE. Accurate nonadiabatic quantum dynamics on the cheap: Making the most of mean field theory with master equations. J Chem Phys 2015; 142:094110. [DOI: 10.1063/1.4913686] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Affiliation(s)
- Aaron Kelly
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
| | - Nora Brackbill
- Department of Physics, Stanford University, Stanford, California 94305, USA
| | - Thomas E. Markland
- Department of Chemistry, Stanford University, Stanford, California 94305, USA
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