Here, we provide links to specific code repositories and data sets used in our publications. We aim at adding further data sets and code samples over time, also from older publications.

You can also check our github repository for code.

For data storage, we currently mostly use the GIN service of the G-Node initiative. For some of our data sets there, small corrections and additions have been made, so it’s best to go to the latest version of the respository under the link “Browse Repository” (rather than referring to the archived version).

  • Krüppel et al 2023: Diversity of ganglion cell responses to saccade-like image shifts in the primate retina
    Data with spike trains of marmoset retinal ganglion cells under stimulation with saccadic grating shifts and supporting stimuli (Citation: Krüppel S, Karamanlis D, Erol YC, Zapp SJ, Gollisch T (2023) Dataset – Marmoset retinal ganglion cell responses to saccadic grating shifts and supporting stimuli. G-Node. https://doi.org/10.12751/g-node.thlt1j)

  • Liu, Karamanlis, Gollisch 2022: Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration
    Data with spike trains of salamander retinal ganglion cells under stimulation with flashed natural images (Citation: Liu JK, Gollisch T (2021) Dataset – Salamander retinal ganglion cell responses to natural images. G-Node. https://doi.org/10.12751/g-node.kod28e)

  • Schreyer, Gollisch 2021: Nonlinearities in retinal bipolar cells shape the encoding of artificial and natural stimuli
    Data with membrane potential recordings of retinal bipolar cells in salamander under various visual stimuli (Citation: Schreyer HM, Gollisch T (2021) Dataset – Salamander retinal bipolar cell membrane potential measured under visual stimulation. G-Node. https://doi.org/10.12751/g-node.fygjxn)

  • Karamanlis, Gollisch 2021: Nonlinear spatial integration underlies the diversity of retinal ganglion cell responses to natural images
    Data with spike times of mouse retinal ganglion cells under natural images and spatial-integration analysis (Citation: Karamanlis D, Gollisch T (2021) Dataset – Mouse retinal ganglion cell responses to natural images and spatial-integration analysis. G-Node. https://doi.org/10.12751/g-node.2j3d2i)

  • Khani, Gollisch 2021: Linear and nonlinear chromatic integration in the mouse retina
    Data with spike times of mouse retinal ganglion cells under stimulation for chromatic-integration analysis (Citation: Khani M, Gollisch T (2021) Dataset – Mouse retinal ganlion cell responses for analysis of chromatic integration. G-Node. https://doi.org/10.12751/g-node.0lngls)
    Code (Matlab) with sample data analysis (recreation of stimuli, evaluation of chromatic integration)

  • Kühn, Gollisch 2019: Activity correlations between direction-selective retinal ganglion cells synergistically enhance motion decoding from complex visual scenes
    Data with spike times of salamander retinal ganglion cells under random-walk motion of a spatial texture (Citation: Kühn NK, Gollisch T (2018) Kuehn_and_Gollisch_RGC_spiketrains_for_moving_texture. G-Node. https://doi.org/10.12751/g-node.0300fd)
    Code (Matlab) for data analysis (trajectory reconstruction, computation of information, canonical correlation analysis)

  • Liu et al. 2017: Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization
    Data with spike times of salamander retinal ganglion cells under finely structured spatiotemporal white-noise stimulation (Citation: Gollisch T, Liu JK (2018) Data: Salamander retinal ganglion cells under finely structured spatio-temporal white noise. G-Node. https://doi.org/10.12751/g-node.62b65b)
    Code (Matlab) for performing spike-triggered non-negative matrix factorization to retreave spatial subunits

  • Khani, Gollisch 2017: Diversity in spatial scope of contrast adaptation among mouse retinal ganglion cells
    Data with spike trains of mouse retinal ganglion cells under stimulation with spatiotemporal white noise, including local switches in contrast (Citation: Khani MH, Gollisch T (2022) Mouse retinal ganglion cell responses to local contrast switches. G-Node. https://doi.org/10.12751/g-node.xygy7j)

  • Bemme, Weick, Gollisch 2017: Differential effects of HCN channel block on On and Off pathways in the retina as a potential cause for medication-induced phosphene perception
    Data with spike times of mouse retinal ganglion cells under various visual stimuli with and without HCN-channel blocker ivabradine (Citation: Gollisch T, Bemme S (2021) Dataset – Mouse retinal ganglion cell responses under ivabradine. G-Node. https://doi.org/10.12751/g-node.d46ab7)

  • Krishnamoorthy et al. 2017: Sensitivity to image recurrence across eye-movement-like image transitions through local serial inhibition in the retina
    Code (Matlab) for the simulation in the paper of an image-recurrence-sensitive cell under saccadic shifts of a grating

  • Onken et al. 2016: Using matrix and tensor factorizations for the single-trial analysis of population spike trains
    Data (Dryad repository) of spike times from salamander retinal ganglion cells under presentation of flashed natural images (Citation: Onken A et al. (2017) Data from: Using matrix and tensor factorization for the single-trial analysis of population spike trains. Dryad, Dataset, https://doi.org/10.5061/dryad.4ch10)

  • Liu, Gollisch 2015: Spike-triggered covariance analysis reveals phenomenological diversity of contrast adaptation in the retina
    Data (Dryad repository) of spike times from salamander retinal ganglion cells under presentation temporal white-noise stimulation and switching of contrast (Citation: Liu JK, Gollisch T (2016). Data from: Spike-triggered covariance analysis reveals phenomenological diversity of contrast adaptation in the retina. Dryad, Dataset, https://doi.org/10.5061/dryad.7r7n7)