32
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Spike-triggered covariance: geometric proof, symmetry properties, and extension beyond Gaussian stimuli

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The space of sensory stimuli is complex and high-dimensional. Yet, single neurons in sensory systems are typically affected by only a small subset of the vast space of all possible stimuli. A proper understanding of the input–output transformation represented by a given cell therefore requires the identification of the subset of stimuli that are relevant in shaping the neuronal response. As an extension to the commonly-used spike-triggered average, the analysis of the spike-triggered covariance matrix provides a systematic methodology to detect relevant stimuli. As originally designed, the consistency of this method is guaranteed only if stimuli are drawn from a Gaussian distribution. Here we present a geometric proof of consistency, which provides insight into the foundations of the method, in particular, into the crucial role played by the geometry of stimulus space and symmetries in the stimulus–response relation. This approach leads to a natural extension of the applicability of the spike-triggered covariance technique to arbitrary spherical or elliptic stimulus distributions. The extension only requires a subtle modification of the original prescription. Furthermore, we present a new resampling method for assessing statistical significance of identified relevant stimuli, applicable to spherical and elliptic stimulus distributions. Finally, we exemplify the modified method and compare it to other prescriptions given in the literature.

          Related collections

          Most cited references51

          • Record: found
          • Abstract: found
          • Article: not found

          Spatio-temporal correlations and visual signalling in a complete neuronal population.

          Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Rapid neural coding in the retina with relative spike latencies.

              Natural vision is a highly dynamic process. Frequent body, head, and eye movements constantly bring new images onto the retina for brief periods, challenging our understanding of the neural code for vision. We report that certain retinal ganglion cells encode the spatial structure of a briefly presented image in the relative timing of their first spikes. This code is found to be largely invariant to stimulus contrast and robust to noisy fluctuations in response latencies. Mechanistically, the observed response characteristics result from different kinetics in two retinal pathways ("ON" and "OFF") that converge onto ganglion cells. This mechanism allows the retina to rapidly and reliably transmit new spatial information with the very first spikes emitted by a neural population.
                Bookmark

                Author and article information

                Contributors
                samengo@cab.cnea.gov.ar
                tim.gollisch@med.uni-goettingen.de
                Journal
                J Comput Neurosci
                J Comput Neurosci
                Journal of Computational Neuroscience
                Springer US (Boston )
                0929-5313
                1573-6873
                15 July 2012
                15 July 2012
                February 2013
                : 34
                : 1
                : 137-161
                Affiliations
                [ ]Centro Atómico Bariloche and Instituto Balseiro, (8400) San Carlos de Bariloche, Río Negro, Argentina
                [ ]Department of Ophthalmology and Bernstein Center for Computational Neuroscience Göttingen, Georg-August University Göttingen, 37073 Göttingen, Germany
                Author notes

                Action Editor: Jonathan David Victor

                Article
                411
                10.1007/s10827-012-0411-y
                3558678
                22798148
                b7a381be-69da-4551-8bb2-5553fb466cb5
                © The Author(s) 2012
                History
                : 15 January 2012
                : 12 May 2012
                : 27 June 2012
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media New York 2013

                Neurosciences
                covariance analysis,linear-nonlinear model,receptive field,spike-triggered average

                Comments

                Comment on this article