Modelling and Analysis of Retinal Ganglion Cells Through System Identification

Dermot Kerr, Martin McGinnity, Sonya Coleman


Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.


  1. Herikstad, R., Baker, J., Lachaux, J.-P., Gray, C. M., & Yen, S.-C. (2011). Natural Movies Evoke Spike Trains with Low Spike Time Variability in Cat Primary Visual Cortex. Journal of Neuroscience, 31(44), 15844-15860. doi:10.1523/JNEUROSCI.5153-10. 2011
  2. De Boer, Kuyper, P. (1968). “Triggered Correlation”. Biomedical ngineering, vol.BME-15, no.3, pp.169- 179. doi: 10.1109/TBME.1968.4502561 Transactions
  3. Sakai,H.M., Naka K.I., Korenberg, M.J. (1988) "Whitenoise analysis in visual neuroscience". Visual Neuroscience, 1, pp 287-296 DOI: 10.1017
  4. Chichilnisky EJ (2001) A simple white noise analysis of neuronal light responses. Network 12(2):199-213.
  5. Talebi, V., Baker, C.L. (2012). "Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness". The Journal of Neuroscience, Vol. 32, No. 5., pp. 1560-1576, doi:10.1523
  6. Marmarelis, P.Z., Naka, K.I. (1972). White-noise analysis of a neuron chain: An application of the wiener theory. Science 175, 1276-1278
  7. Victor, J., Shapley, R., Knight, B. (1977). Nonlinear analysis of cat retinal ganglion cells in the frequency domain. Proc. Natl. Acad. Sci. U.S.A. 74(7), 3068- 3072
  8. Victor, J. (1979) Nonlinear systems analysis: comparison of white noise and sum of sinusoids in a biological system. Proc. Natl. Acad. Sci. U.S.A. 76(2), 996-998
  9. Marmarelis, V. (2004) Nonlinear Dynamic Modeling of Physiological Systems. Wiley Interscience, Hoboken.
  10. Korenberg, M., Hunter, I. (1996). The identification of nonlinear biological systems: Volterra kernel approaches. Ann. Biomed. Eng. 24(2), 250-268.
  11. Marmarelis VZ, Zhao X. (1997). Volterra models and three-layer perceptions. IEEE Trans Neural Networks 8:1421.
  12. Block-oriented Nonlinear System Identification (2010), Lecture Notes in Control and Information Sciences, Springer Berlin / Heidelberg, Vol. 404. Giri, F. and Bai E.W. Eds
  13. Nehmzow, U. (2006) Scientific Methods in Mobile Robotics: quantitative analysis of agent behaviour. Springer, 2006.
  14. Ostojic S, Brunel N (2011) From Spiking Neuron Models to Linear-Nonlinear Models. PLoS Comput Biol 7(1): e1001056. doi:10.1371/journal.pcbi.1001056
  15. Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci.23;25(47):11003-13.
  16. Korenberg MJ. (1991). Parallel cascade identification and kernel estimation for nonlinear systems. Ann Biomed Eng 19:429.
  17. Pillow JW, Shlens J, Paninski L, Sher A, Litke AM, Chichilnisky EJ, Simoncelli EP. (2008) Spatio temporal correlations and visual signaling in a complete neuronal population. Nature 454: 995-999
  18. Billings SA, Voon WSF. 1984. Least-squares parameter estimation algorithms for non-linear systems. Int.J Systems Sci 15:601.
  19. Friederich, U., Coca, D., Billings, S.A., Juusola, M. (2009) Data Modelling for Analysis of Adaptive Changes in Fly Photoreceptors. Proceedings of the 16th International Conference on Neural Information Processing: Part I
  20. Z. Song, S.A. Billings, D. Coca, M. Postma, R.C. Hardie, & M. Juusola. (2009), Biophysical Modeling of a Drosophila Photoreceptor. LNCS (ICONIP 2009, Part I) 5863: 57-71.
  21. Kerr, D, Nehmzow, U and Billings, S.A. (2010) Towards Automated Code Generation for Autonomous Mobile Robots. In: The Third Conference on Artificial General Intelligence, Lugano. Switzerland. Atlantis Press, Scientific Publishing, Paris, France. 5 pp.
  22. Billings, S.A. Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. John Wiley & Sons, 2013.
  23. Lau B, Stanley GB, Dan Y (2002) Computational subunits of visual cortical neurons revealed by artificial neural networks. Proc Natl Acad Sci U S A 99: 8974-8979.
  24. Prenger, R., Wu, M.C.K., David, S.V., Gallant, J.L., (2004) Nonlinear V1 responses to natural scenes revealed by neural network analysis, Neural Networks, Volume 17, Issues 5-6, Pages 663-679, 10.1016/ j.neunet.2004.03.008.

Paper Citation

in Harvard Style

Kerr D., McGinnity M. and Coleman S. (2014). Modelling and Analysis of Retinal Ganglion Cells Through System Identification . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 158-164. DOI: 10.5220/0005069701580164

in Bibtex Style

author={Dermot Kerr and Martin McGinnity and Sonya Coleman},
title={Modelling and Analysis of Retinal Ganglion Cells Through System Identification},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},

in EndNote Style

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Modelling and Analysis of Retinal Ganglion Cells Through System Identification
SN - 978-989-758-054-3
AU - Kerr D.
AU - McGinnity M.
AU - Coleman S.
PY - 2014
SP - 158
EP - 164
DO - 10.5220/0005069701580164