scholarly journals Recording from defined populations of retinal ganglion cells using a high-density CMOS-integrated microelectrode array with real-time switchable electrode selection

2012 ◽  
Vol 211 (1) ◽  
pp. 103-113 ◽  
Author(s):  
Michele Fiscella ◽  
Karl Farrow ◽  
Ian L. Jones ◽  
David Jäckel ◽  
Jan Müller ◽  
...  
Keyword(s):  
Real Time ◽  
Retinal Ganglion Cells ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
High Density ◽  
Retinal Ganglion ◽  
Electrode Selection

scholarly journals REGISTRO DE ACTIVIDAD ELÉCTRICA EN LA RETINA DE UNA RATA ALBINA EMPLEANDO UNA MATRIZ DE MICROELECTRODOS

2015 ◽  
Vol 20 (3) ◽  
pp. 37-46
Author(s):  
Edwin Alexander CERQUERA ◽  
Jeimy MUÑOZ ◽  
Joaquín ARAYA ◽  
Olivero GÓMEZ
Keyword(s):  
Retinal Ganglion Cells ◽  
Action Potentials ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
Wide Spectrum ◽  
Computational Method ◽  
Spike Sorting ◽  
Retinal Ganglion ◽  
Albino Rat

<p>Las matrices de microelectrodos son dispositivos que permiten la detección de potenciales de acción o espigas en poblaciones de células excitables, ofreciendo varias aplicaciones en el campo de las neurociencias y la biología. Este trabajo muestra un protocolo para el registro de espigas en una población de células ganglionares retinales empleando una matriz de microelectrodos. La retina de una rata albina fue extraída y preparada para ser estimulada <em>in vitro </em>con luz led blanca, con el fin de registrar sus espigas evocadas ante estos estímulos. Cada microelectrodo puede registrar espigas de más de una célula ganglionar, razón por la cual se determinó a qué célula pertenece cada espiga aplicando un procedimiento conocido como “clasificación de espigas”. El trabajo permitió obtener el registro de un periodo de estimulación y otro de no estimulación, con el fin de representar los potenciales de acción evocados con luz y los espontáneos. Los registros fueron almacenados para visualizar las espigas de las células ganglionares y poder aplicar la herramienta de clasificación de espigas. De este modo, se almacenan los instantes de tiempo en los cuales cada célula ganglionar registrada generó potenciales de acción. Este trabajo conllevó al establecimiento de un protocolo de experimentación básico enfocado al uso de matrices MEA en el laboratorio de adquisición de potenciales extracelulares de la Universidad Antonio Nariño Sede Bogotá, no sólo para caracterizar los potenciales de acción de células ganglionares retinales, sino también para otro tipo de células que puedan ser estudiadas empleando matrices de microelectrodos.</p><p align="center"><strong>Recording of Electrical Activity in the Retina of an Albino Rat Employing a Microelectrode Array</strong></p><p>The microelectrode arrays (MEA) are devices that allow the detection of action potentials or spikes in populations of excitable cells, offering a wide spectrum of applications in topics of Neurosciences and Biology. This work describes a protocol for recording of spikes in a population of retinal ganglion cells employing a microelectrode array. The retina of an albino rat was dissected and prepared to be stimulated<em> in vitro </em>with white led light and to record their evoked spikes. Each microelectrode can record spikes from more than a ganglion cell, for which it was necessary to determine which cell fires each spike applying a procedure known as spike sorting. The work allowed to obtain the recording of a stimulation period and another of non-stimulation, representing evoked and spontaneous action potentials. The recordings were saved, in order to visualize the action potentials of the ganglion cells detected  and to apply a computational method for the spike sorting. In this way, it was saved the time stamps in which each action potential was fired by its respective cell. This work established a basic experimentation protocol focused to the use of MEA devices in the laboratory for acquisition of extracellular potentials at the Antonio Nariño University – Bogota Headquarters, not only for characterization of action potentials fired by retinal ganglion cells populations, but also for other kind of cells that can be studied employing MEA devices.</p><p> </p>

Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses

2020 ◽  
Vol 30 (09) ◽  
pp. 2050045 ◽  
Author(s):  
Antonio Lozano ◽  
Juan Sebastián Suárez ◽  
Cristina Soto-Sánchez ◽  
Javier Garrigós ◽  
J. Javier Martínez-Alvarez ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Deep Learning ◽  
Retinal Ganglion Cells ◽  
Computational Models ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
Semantic Segmentation ◽  
Video Stream ◽  
Retinal Ganglion ◽  
Task Oriented

Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering and artificial vision comes with new theories and tools that, along with the dawn of modern artificial intelligence, constitute a promising framework for the further development of neurotechnology. In the framework of the development of a Cortical Visual Neuroprosthesis for the blind (CORTIVIS), we are now facing the challenge of developing not only computationally powerful tools and flexible approaches that will allow us to provide some degree of functional vision to individuals who are profoundly blind. In this work, we propose a general neuroprosthesis framework composed of several task-oriented and visual encoding modules. We address the development and implementation of computational models of the firing rates of retinal ganglion cells and design a tool — Neurolight — that allows these models to be interfaced with intracortical microelectrodes in order to create electrical stimulation patterns that can evoke useful perceptions. In addition, the developed framework allows the deployment of a diverse array of state-of-the-art deep-learning techniques for task-oriented and general image pre-processing, such as semantic segmentation and object detection in our system’s pipeline. To the best of our knowledge, this constitutes the first deep-learning-based system designed to directly interface with the visual brain through an intracortical microelectrode array. We implement the complete pipeline, from obtaining a video stream to developing and deploying task-oriented deep-learning models and predictive models of retinal ganglion cells’ encoding of visual inputs under the control of a neurostimulation device able to send electrical train pulses to a microelectrode array implanted at the visual cortex.

scholarly journals A facile and comprehensive algorithm for electrical response identification in mouse retinal ganglion cells

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246547
Author(s):  
Wanying Li ◽  
Shan Qin ◽  
Yijie Lu ◽  
Hao Wang ◽  
Zhen Xu ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Retinal Ganglion Cells ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
Ex Vivo ◽  
Electrical Response ◽  
Pulse Amplitude ◽  
Retinal Ganglion ◽  
Pulse Parameters ◽  
High Consistency

Retinal prostheses can restore the basic visual function of patients with retinal degeneration, which relies on effective electrical stimulation to evoke the physiological activities of retinal ganglion cells (RGCs). Current electrical stimulation strategies have defects such as unstable effects and insufficient stimulation positions, therefore, it is crucial to determine the optimal pulse parameters for precise and safe electrical stimulation. Biphasic voltages (cathode-first) with a pulse width of 25 ms and different amplitudes were used to ex vivo stimulate RGCs of three wild-type (WT) mice using a commercial microelectrode array (MEA) recording system. An algorithm is developed to automatically realize both spike-sorting and electrical response identification for the spike signals recorded. Measured from three WT mouse retinas, the total numbers of RGC units and responsive RGC units were 1193 and 151, respectively. In addition, the optimal pulse amplitude range for electrical stimulation was determined to be 0.43 V-1.3 V. The processing results of the automatic algorithm we proposed shows high consistency with those using traditional manual processing. We anticipate the new algorithm can not only speed up the elaborate electrophysiological data processing, but also optimize pulse parameters for the electrical stimulation strategy of neural prostheses.

scholarly journals Strong and specific connections between retinal axon mosaics and midbrain neurons revealed by large scale paired recordings

2021 ◽  
Author(s):  
Jérémie Sibille ◽  
Carolin Gehr ◽  
Jonathan I. Benichov ◽  
Hymavathy Balasubramanian ◽  
Kai Lun Teh ◽  
...  
Keyword(s):  
Single Cell ◽  
Retinal Ganglion Cells ◽  
Large Scale ◽  
Ganglion Cells ◽  
Brain Regions ◽  
High Density ◽  
List Type ◽  
Retinal Ganglion ◽  
Midbrain Neurons

SUMMARYThe superior colliculus (SC) is a midbrain structure that plays important roles in visually guided behaviors. Neurons in the SC receive afferent inputs from retinal ganglion cells (RGC), the output cells of the retina, but how SC neurons integrate RGC activity in vivo is unknown. SC neurons might be driven by strong but sparse retinal inputs, thereby reliably transmitting specific retinal functional channels. Alternatively, SC neurons could sum numerous but weak inputs, thereby extracting new features by combining a diversity of retinal signals. Here, we discovered that high-density electrodes simultaneously capture the activity and the location of large populations of retinal axons and their postsynaptic SC target neurons, permitting us to investigate the retinocollicular circuit on a structural and functional level in vivo. We show that RGC axons in the mouse are organized in mosaics that provide a single cell precise representation of the retina as input to SC. This isomorphic mapping between retina and SC builds the scaffold for highly specific wiring in the retinocollicular circuit which we show is characterized by strong connections and limited functional convergence, established in log-normally distributed connection strength. Because our novel method of large-scale paired recordings is broadly applicable for investigating functional connectivity across brain regions, we were also able to identify retinal inputs to the avian optic tectum of the zebra finch. We found common wiring rules in mammals and birds that provide a precise and reliable representation of the visual world encoded in RGCs to neurons in retinorecipient areas.HIGHLIGHTSHigh-density electrodes capture the activity of afferent axons and target neurons in vivoRetinal ganglion cells axons are organized in mosaicsSingle cell precise isomorphism between dendritic and axonal RGC mosaicsMidbrain neurons are driven by sparse but strong retinal inputsFunctional wiring of the retinotectal circuit is similar in mammals and birds

Spatial resolution, contrast sensitivity, and sensitivity to defocus of chicken retinal ganglion cells in vitro

2009 ◽  
Vol 26 (5-6) ◽  
pp. 467-476 ◽  
Author(s):  
ERICH DIEDRICH ◽  
FRANK SCHAEFFEL
Keyword(s):  
Contrast Sensitivity ◽  
Retinal Ganglion Cells ◽  
Spatial Resolution ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
Amacrine Cells ◽  
Retinal Ganglion ◽  
Modulation Transfer ◽  
Eye Growth

AbstractThe chicken has been extensively studied as an animal model for myopia because its eye growth is tightly controlled by visual experience. It has been found that the retina controls the axial eye growth rates depending on the amount and the sign of defocus imposed in the projected image. Glucagonergic amacrine cells were discovered that appear to encode for the sign of imposed defocus. It is not clear whether the downstream neurons, the retinal ganglion cells, still have access to this information—and whether it ultimately reaches the brain. We have analyzed the spike rates of chicken retinal ganglion cells in vitro using a microelectrode array. For this purpose, we initially defined spatial resolution and contrast sensitivity in vitro. Two classes of chicken retinal ganglions were found, depending on the linearity of their responses with increasing contrast. Responses generally declined with increasing defocus of the visual stimulus. These responses were well predicted by the modulation transfer function for a diffraction-limited defocused optical system, the first Bessel function. Thus, the studied retinal ganglion cells did not distinguish between a loss of contrast at a given spatial frequency due to reduced contrast of the stimulus pattern or because the pattern was presented out of focus. Furthermore, there was no indication that the retinal ganglion cells responded differently to defocus of either sign, at least for the cells that were recorded in this study.

scholarly journals Non-parametric physiological classification of retinal ganglion cells in the mouse retina

2018 ◽  
Author(s):  
Jonathan Jouty ◽  
Gerrit Hilgen ◽  
Evelyne Sernagor ◽  
Matthias H. Hennig
Keyword(s):  
Retinal Ganglion Cells ◽  
Visual Information ◽  
Large Scale ◽  
Distance Measure ◽  
Ganglion Cells ◽  
High Density ◽  
Mouse Retina ◽  
Retinal Ganglion ◽  
Electrode Arrays

Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities.

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