Computational modelling of salamander retinal ganglion cells using machine learning approaches

Gautham P. Das, Philip J. Vance, Dermot Kerr, Sonya A. Coleman, Thomas M. McGinnity, Jian K. Liu

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung


Artificial vision using computational models that can mimic biological vision is an area of ongoing research. One of the main themes within this research is the study of the retina and in particular, retinal ganglion cells which are responsible for encoding the visual stimuli. A common approach to modelling the internal processes of retinal ganglion cells is the use of a linear – non-linear cascade model, which models the cell's response using a linear filter followed by a static non-linearity. However, the resulting model is generally restrictive as it is often a poor estimator of the neuron's response. In this paper we present an alternative to the linear – non-linear model by modelling retinal ganglion cells using a number of machine learning techniques which have a proven track record for learning complex non-linearities in many different domains. A comparison of the model predicted spike rate shows that the machine learning models perform better than the standard linear – non-linear approach in the case of temporal white noise stimuli.

Seiten (von - bis)101-112
PublikationsstatusVeröffentlicht - 24 Jan 2019



    ASJC Scopus subject areas

    • !!Computer Science Applications
    • Kognitive Neurowissenschaft
    • Artificial intelligence

    Dieses zitieren

    Das, G. P., Vance, P. J., Kerr, D., Coleman, S. A., McGinnity, T. M., & Liu, J. K. (2019). Computational modelling of salamander retinal ganglion cells using machine learning approaches. Neurocomputing, 325, 101-112.