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Tipado Z, Kuypers KPC, Sorger B, Ramaekers JG. Visual hallucinations originating in the retinofugal pathway under clinical and psychedelic conditions. Eur Neuropsychopharmacol 2024; 85:10-20. [PMID: 38648694 DOI: 10.1016/j.euroneuro.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 04/11/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
Psychedelics like LSD (Lysergic acid diethylamide) and psilocybin are known to modulate perceptual modalities due to the activation of mostly serotonin receptors in specific cortical (e.g., visual cortex) and subcortical (e.g., thalamus) regions of the brain. In the visual domain, these psychedelic modulations often result in peculiar disturbances of viewed objects and light and sometimes even in hallucinations of non-existent environments, objects, and creatures. Although the underlying processes are poorly understood, research conducted over the past twenty years on the subjective experience of psychedelics details theories that attempt to explain these perceptual alterations due to a disruption of communication between cortical and subcortical regions. However, rare medical conditions in the visual system like Charles Bonnet syndrome that cause perceptual distortions may shed new light on the additional importance of the retinofugal pathway in psychedelic subjective experiences. Interneurons in the retina called amacrine cells could be the first site of visual psychedelic modulation and aid in disrupting the hierarchical structure of how humans perceive visual information. This paper presents an understanding of how the retinofugal pathway communicates and modulates visual information in psychedelic and clinical conditions. Therefore, we elucidate a new theory of psychedelic modulation in the retinofugal pathway.
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Affiliation(s)
- Zeus Tipado
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands.
| | - Kim P C Kuypers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Johannes G Ramaekers
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
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Idrees S, Manookin MB, Rieke F, Field GD, Zylberberg J. Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation. Nat Commun 2024; 15:5957. [PMID: 39009568 PMCID: PMC11251147 DOI: 10.1038/s41467-024-50114-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/28/2024] [Indexed: 07/17/2024] Open
Abstract
Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on 'Deep Retina,' a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting male and female primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.
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Affiliation(s)
- Saad Idrees
- Department of Physics and Astronomy, York University, Toronto, ON, Canada.
- Centre for Vision Research, York University, Toronto, ON, Canada.
| | | | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Greg D Field
- Stein Eye Institute, Department of Ophthalmology, University of California, Los Angeles, CA, USA
| | - Joel Zylberberg
- Department of Physics and Astronomy, York University, Toronto, ON, Canada.
- Centre for Vision Research, York University, Toronto, ON, Canada.
- Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON, Canada.
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Vėbraitė I, Bar-Haim C, David-Pur M, Hanein Y. Bi-directional electrical recording and stimulation of the intact retina with a screen-printed soft probe: a feasibility study. Front Neurosci 2024; 17:1288069. [PMID: 38264499 PMCID: PMC10804455 DOI: 10.3389/fnins.2023.1288069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 12/14/2023] [Indexed: 01/25/2024] Open
Abstract
Introduction Electrophysiological investigations of intact neural circuits are challenged by the gentle and complex nature of neural tissues. Bi-directional electrophysiological interfacing with the retina, in its intact form, is particularly demanding and currently there is no feasible approach to achieve such investigations. Here we present a feasibility study of a novel soft multi-electrode array suitable for bi-directional electrophysiological study of the intact retina. Methods Screen-printed soft electrode arrays were developed and tested. The soft probes were designed to accommodate the curvature of the retina in the eye and offer an opportunity to study the retina in its intact form. Results For the first time, we show both electrical recording and stimulation capabilities from the intact retina. In particular, we demonstrate the ability to characterize retina responses to electrical stimulation and reveal stable, direct, and indirect responses compared with ex-vivo conditions. Discussion These results demonstrate the unique performances of the new probe while also suggesting that intact retinas retain better stability and robustness than ex-vivo retinas making them more suitable for characterizing retina responses to electrical stimulation.
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Affiliation(s)
- Ieva Vėbraitė
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Chen Bar-Haim
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Moshe David-Pur
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yael Hanein
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Trapani F, Spampinato GLB, Yger P, Marre O. Differences in nonlinearities determine retinal cell types. J Neurophysiol 2023; 130:706-718. [PMID: 37584082 DOI: 10.1152/jn.00243.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023] Open
Abstract
Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record from a large number of cells to classify ganglion cells into different types based on functional information. Although the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to stimuli. These two approaches have not been compared directly. Here, we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into functional groups, one based on the receptive field properties, and the other one using directly their responses to stimuli with various temporal frequencies. We show that the response-based approach allows separation of more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the response-based method takes into account not only the linear part of ganglion cell function but also some of the nonlinearities. A careful characterization of nonlinear processing is thus key to allowing functional classification of sensory neurons.NEW & NOTEWORTHY In the retina, ganglion cells can be classified based on their response to visual stimuli. Although some methods are based on the modeling of receptive fields, others rely on responses to characteristic stimuli. We compared these two classes of methods and show that the latter provides a higher discrimination performance. We also show that this gain arises from the ability to account for the nonlinear behavior of neurons.
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Affiliation(s)
- Francesco Trapani
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France
| | | | - Pierre Yger
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France
| | - Olivier Marre
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France
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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] [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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Do MTH. Individual variations of visual information. Neuron 2022; 110:564-565. [PMID: 35176239 DOI: 10.1016/j.neuron.2022.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
In this issue of Neuron, Shah et al. reveal that coding of visual space by the primate retina varies across individuals and sexes. A computational model provides insight into themes and variations of coding. Implications for sight restoration are explored.
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Affiliation(s)
- Michael Tri H Do
- F.M. Kirby Neurobiology Center, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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