Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings

Portelli, Geoffrey; Barrett, John M.; Hilgen, Gerrit; Masquelier, Timothée; Maccione, Alessandro; Marco, Stefano Di; Berdondini, Luca; Kornprobst, Pierre; Sernagor, Evelyne

doi:10.1523/eneuro.0134-15.2016

Cited by 45 publications

(49 citation statements)

References 42 publications

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“…A main caveat in previous studies was that RGC recordings from multiple preparations were pooled together to obtain a data set of sufficient size. Large scale, high density microelectrode arrays (MEAs) now make it possible to record large populations comprising thousands of cells from a single retina (18)(19)(20)(21). This reduces contaminating effects of variability between animals and experimental conditions, and allows precise control of stimulation for all recorded neurons.…”

Section: Introductionmentioning

confidence: 99%

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

Jouty

Hilgen

Sernagor

et al. 2018

Preprint

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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. retinal ganglion cells | multi-electrode array | light responses | classification | spike distanceCorrespondence: m.hennig@ed.ac.uk

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Section: Introductionmentioning

confidence: 99%

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

Jouty

Hilgen

Sernagor

et al. 2018

Preprint

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“…Visual stimuli (664x664 pixel images for a total area of 2.67x2.67 mm) were presented using a custom built high-resolution photostimulation system based on a DLP video projector (lightCrafter, Texas Instruments, USA) combined with a custom made photostimulation software and synchronized with the recording system (Portelli et al, 2016). Neutral density lters (4.5 -1.9) were used to control the amount of light falling on the retina.…”

Section: Visual and Optogenetic Stimulationmentioning

confidence: 99%

Unsupervised spike sorting for large scale, high density multielectrode arrays

Hilgen

Sorbaro

Pirmoradian

et al. 2016

Preprint

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SUMMARYWe present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, lowdimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.

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“…Yet, if we accept as true that the behavior of a system emerges from the interactions of its fundamental processing units, the model has sooner or later to be downscaled to this level to provide a more faithful and reliable description. This point has been very often discussed in the past and is still a matter of debate [Brette, 2015] but it seems that individual or relative spike-timing plays a major role in neural information encoding in different ways [Portelli et al, 2016, Jacobs et al, 2009, Saal et al, 2015, Moyer et al, 2014. Models relying on individual spiking neurons and of which construction is constrained by anatomical and physiological evidence of complex connectivity architectures often provide fruitful results because they directly link a detailed circuitry to its assumed function or behavior [Chersi et al, 2013, Mandali et al, 2015, Medina et al, 2001, Yang et al, 2015.…”

Section: Extinction Protocolmentioning

confidence: 99%

Decision making under uncertainty in a spiking neural network model of the basal ganglia

Héricé

Khalil

Moftah

et al. 2016

J. Integr. Neurosci.

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The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used these properties to develop and extend a connectionist model at a spiking neuron level based on a previous rate model approach. This model is demonstrated on decision-making tasks that have been studied in primates and the electrophysiology interpreted to show that the decision is made in two steps. To model this, we have used two parallel loops, each of which performs decision-making based on interactions between positive and negative feedback pathways. This model is able to perform two-level decision-making as in primates. We show here that, before learning, synaptic noise is sufficient to drive the decision-making process and that, after learning, the decision is based on the choice that has proven most likely to be rewarded. The model is then submitted to lesion tests, reversal learning and extinction protocols. We show that, under these conditions, it behaves in a consistent manner and provides predictions in accordance with observed experimental data.

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Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings

Abstract: Visual Abstract

Cited by 45 publications

References 42 publications

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

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

Unsupervised spike sorting for large scale, high density multielectrode arrays

Decision making under uncertainty in a spiking neural network model of the basal ganglia

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