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Non-Centered Spike-Triggered Covariance Analysis Reveals Neurotrophin-3 as a Developmental Regulator of Receptive Field Properties of ON-OFF Retinal Ganglion Cells

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      Abstract

      The functional separation of ON and OFF pathways, one of the fundamental features of the visual system, starts in the retina. During postnatal development, some retinal ganglion cells (RGCs) whose dendrites arborize in both ON and OFF sublaminae of the inner plexiform layer transform into RGCs with dendrites that monostratify in either the ON or OFF sublamina, acquiring final dendritic morphology in a subtype-dependent manner. Little is known about how the receptive field (RF) properties of ON, OFF, and ON-OFF RGCs mature during this time because of the lack of a reliable and efficient method to classify RGCs into these subtypes. To address this deficiency, we developed an innovative variant of Spike Triggered Covariance (STC) analysis, which we term Spike Triggered Covariance – Non-Centered (STC-NC) analysis. Using a multi-electrode array (MEA), we recorded the responses of a large population of mouse RGCs to a Gaussian white noise stimulus. As expected, the Spike-Triggered Average (STA) fails to identify responses driven by symmetric static nonlinearities such as those that underlie ON-OFF center RGC behavior. The STC-NC technique, in contrast, provides an efficient means to identify ON-OFF responses and quantify their RF center sizes accurately. Using this new tool, we find that RGCs gradually develop sensitivity to focal stimulation after eye opening, that the percentage of ON-OFF center cells decreases with age, and that RF centers of ON and ON-OFF cells become smaller. Importantly, we demonstrate for the first time that neurotrophin-3 (NT-3) regulates the development of physiological properties of ON-OFF center RGCs. Overexpression of NT-3 leads to the precocious maturation of RGC responsiveness and accelerates the developmental decrease of RF center size in ON-OFF cells. In summary, our study introduces STC-NC analysis which successfully identifies subtype RGCs and demonstrates how RF development relates to a neurotrophic driver in the retina.

      Author Summary

      The developmental separation of ON and OFF pathways is one of the fundamental features of the visual system. In the mouse retina, some bi-stratified ON-OFF RGCs are refined into mono-stratified ON or OFF RGCs during the first postnatal month. However, the process by which the RGCs' physiological receptive field properties mature remains incompletely characterized, mainly due to the lack of a reliable and efficient method to classify RGCs into different subtypes. Here we have developed an innovative analysis, Spike Triggered Covariance – Non-Centered (STC-NC), and demonstrated that this technique can accurately characterize the receptive field properties of ON, OFF and ON-OFF center cells. We show that, in wildtype mouse, RGCs gradually develop sensitivity to focal stimulation after eye opening, and the development of ON-OFF receptive field center properties correlates well with their dendritic laminar refinement. Furthermore, overexpression of NT-3 accelerates the developmental decrease of receptive field center size in ON-OFF cells. Our study is the first to establish the STC-NC analysis which can successfully identify ON-OFF subtype RGCs and to demonstrate how receptive field development relates to a neurotrophic driver in the retina.

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      Most cited references 50

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      Neurotrophins: roles in neuronal development and function.

      Neurotrophins regulate development, maintenance, and function of vertebrate nervous systems. Neurotrophins activate two different classes of receptors, the Trk family of receptor tyrosine kinases and p75NTR, a member of the TNF receptor superfamily. Through these, neurotrophins activate many signaling pathways, including those mediated by ras and members of the cdc-42/ras/rho G protein families, and the MAP kinase, PI-3 kinase, and Jun kinase cascades. During development, limiting amounts of neurotrophins function as survival factors to ensure a match between the number of surviving neurons and the requirement for appropriate target innervation. They also regulate cell fate decisions, axon growth, dendrite pruning, the patterning of innervation and the expression of proteins crucial for normal neuronal function, such as neurotransmitters and ion channels. These proteins also regulate many aspects of neural function. In the mature nervous system, they control synaptic function and synaptic plasticity, while continuing to modulate neuronal survival.
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        A simple white noise analysis of neuronal light responses.

        A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.
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          Do we know what the early visual system does?

          We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.
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            Author and article information

            Affiliations
            [1 ]Interdepartmental Neuroscience Program, Northwestern University, Evanston, Illinois, United States of America
            [2 ]Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States of America
            [3 ]Department of Neurobiology and Physiology, Northwestern University, Evanston, Illinois, United States of America
            Université Paris Descartes, Centre National de la Recherche Scientifique, France
            Author notes

            Conceived and designed the experiments: DRC XL. Performed the experiments: DRC XL. Analyzed the data: DRC JC JBT XL. Contributed reagents/materials/analysis tools: JC JBT XL. Wrote the paper: DRC JC JBT XL.

            Contributors
            Role: Editor
            Journal
            PLoS Comput Biol
            plos
            ploscomp
            PLoS Computational Biology
            Public Library of Science (San Francisco, USA )
            1553-734X
            1553-7358
            October 2010
            October 2010
            21 October 2010
            : 6
            : 10
            2958799
            20975932
            10-PLCB-RA-2268R2
            10.1371/journal.pcbi.1000967
            (Editor)
            Cantrell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 16
            Categories
            Research Article
            Computational Biology/Computational Neuroscience
            Developmental Biology/Neurodevelopment
            Neuroscience/Neurodevelopment
            Physiology/Neuronal Signaling Mechanisms
            Physiology/Sensory Systems

            Quantitative & Systems biology

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