A simple model of retina-LGN transmission
- 289 Downloads
To gain a deeper understanding of the transmission of visual signals from retina through the lateral geniculate nucleus (LGN), we have used a simple leaky integrate and-fire model to simulate a relay cell in the LGN. The simplicity of the model was motivated by two questions: (1) Can an LGN model that is driven by a retinal spike train recorded as synaptic (‘S’) potentials, but does not include a diverse array of ion channels, nor feedback inputs from the cortex, brainstem, and thalamic reticular nucleus, accurately simulate the LGN discharge on a spike-for-spike basis? (2) Are any special synaptic mechanisms, beyond simple summation of currents, necessary to model experimental recordings? We recorded cat relay cell responses to spatially homogeneous small or large spots, with luminance that was rapidly modulated in a pseudo-random fashion. Model parameters for each cell were optimized with a Simplex algorithm using a short segment of the recording. The model was then tested on a much longer, distinct data set consisting of responses to numerous repetitions of the noisy stimulus. For LGN cells that spiked in response to a sufficiently large fraction of retinal inputs, we found that this simplified model accurately predicted the firing times of LGN discharges. This suggests that modulations of the efficacy of the retino-geniculate synapse by pre-synaptic facilitation or depression are not necessary in order to account for the LGN responses generated by our stimuli, and that post-synaptic summation is sufficient.
KeywordsLGN model Retinogeniculate transmission Integrate and fire S potentials Vision
Unable to display preview. Download preview PDF.
- Bishop, P. O. (1953). Synaptic transmission; an analysis of the electrical activity of the lateral geniculate nucleus in the cat after optic nerve stimulation. Proceedings of the Royal Society of London B, Biological Sciences, 141(904), 362–392.Google Scholar
- Carandini, M., Demb, J. B., Mante, V., Tolhurst, D. J., Dan, Y., Olshausen, B. A., et al. (2005). Do we know what the early visual system does? Journal of Neurophysiology, 25(46), 10577–10597.Google Scholar
- Carandini, M., Horton, J. C., & Sincich, L. C. (2006). Postsynaptic mechanisms converting retinal spike trains into geniculate spike trains. Society for Neuroscience Abstracts, 32, 11.14.Google Scholar
- Casti, A. R. R., Kaplan, E., Lubliner, K., & Xiao, Y. (2005). Effects of cortical feedback on the lgn: Information transmission and dynamics. Society for Neuroscience Abstracts 31, 506.19.Google Scholar
- Chen, C., & Regehr, W. G. (2003). Presynaptic modulation of the retinogeniculate synapse. Journal of Neurophysiology, 23(8), 3130–3135.Google Scholar
- Kistler, W., Gerstner, W., & Hemmen, J. L. V. (1997). Reduction of the Hodgkin–Huxley equations to a single-variable threshold model. Neural Computation, 9, 1015–1045.Google Scholar
- Koch, C. (1985). Understanding the intrinsic circuitry of the cat’s lateral geniculate nucleus: electrical properties of the spine-triad arrangement. Proceedings of the Royal Society of London B, Biological Sciences, 225(1240), 365–390.Google Scholar
- Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7, 308–313.Google Scholar
- Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1992). Numerical Recipes: The Art of Scientific Computing (2nd ed.). Cambridge University Press.Google Scholar
- Sirovich, L. (2007). Populations of tightly coupled neurons: The RGC/LGN system. Neural Computation (accepted).Google Scholar
- Sutter, E. E. (1987). A practical nonstochastic approach to nonlinear time-domain analysis. In V. Z. Marmarelis (Ed.), Advanced Methods of Physiological Systems Modeling (vol. 1). University of Southern California, Los Angeles.Google Scholar