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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)101-112
Number of pages12
Publication statusPublished - 24 Jan 2019


  • Artificial vision
  • Biological vision
  • Machine learning
  • Retinal Ganglion cell

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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