TY - JOUR
T1 - Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques
AU - Vance, Philip
AU - Das, Gautham
AU - Kerr, Dermot
AU - Coleman, Sonya
AU - McGinnity, T.Martin
AU - Gollisch, Tim
AU - Liu, Jian
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Retinal ganglion cells
KW - computational modelling
KW - biological vision
KW - receptive field
KW - artificial stimuli.
UR - https://pure.ulster.ac.uk/en/publications/bio-inspired-approach-to-modelling-retinal-ganglion-cells-using-s-3
U2 - 10.1109/TNNLS.2017.2690139
DO - 10.1109/TNNLS.2017.2690139
M3 - Article
C2 - 28422669
VL - 29
SP - 1796
EP - 1808
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 5
ER -