The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power and performance. A key aspect to modelling the human visual system is the ability to accurately model the behaviour and computation within the retina. In particular, we focus on modelling the retinal ganglion cells as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within retinal ganglion cells can be derived by quantitatively fitting sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modelled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this work, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behaviour, and are a viable alternative to traditional linear-nonlinear approaches.
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Early online date||12 Apr 2017|
|Publication status||Published - May 2018|
- Retinal ganglion cells
- computational modelling
- biological vision
- receptive field
- artificial stimuli.