Volume 10 Supplement 1

Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access

How is stimulus processing of the lateral geniculate nucleus derived from its input(s)?

BMC Neuroscience200910(Suppl 1):P125

DOI: 10.1186/1471-2202-10-S1-P125

Published: 13 July 2009

LGN neurons can respond with extreme precision to a variety of temporally varying stimuli [1]. This precision requires non-linear processing of the stimulus and therefore cannot be described by standard linear (or linear-non-linear, LN) models. Rather, in previous work, we have found that precision arises through the interplay of an excitatory receptive field and a similarly tuned – but delayed – suppressive receptive field, allowing for fine time scales in the LGN response to arise in the brief window where excitation exceeds the suppression [2]. However, it is not clear whether such non-linear interaction arises in the retina, at the retinogeniculate synapse itself or involves other secondary LGN inputs.

To investigate this, we applied a newly developed a Generalized Non-Linear Modeling (GNLM) framework to data involving the simultaneous recording of LGN neurons and their predominant retinal ganglion cell (RGC) input. This framework uses efficient maximum-likelihood optimization [3], adapted to include nested non-linear terms [2, 4]. Using this novel approach, we simultaneously optimize the shape of postsynaptic currents resulting from RGC stimulation along with other non-linear excitatory and inhibitory elements tuned to the visual stimulus, based on the observed RGC and LGN spike trains alone. We also can directly characterize the non-linear elements in the RGC.

We found that while there were subtle non-linear elements in the RGC response, they were amplified in that of the LGN. Consistent with previous reports [5], summation with a threshold explains a large part of the increased sparseness of LGN responses relative to those of the input RGC. However, an additional opposite-sign suppressive term was also present, possibly contributing to the push-pull nature of the LGN response observed in intracellular recordings [6]. In many cases, we also detected additional non-linear excitatory inputs, possibly resulting from other RGC inputs. Interestingly, such additional terms were much more sensitive to contrast than the dominant input, possible resulting in the well-known contrast gain control effects, though present both at the level of the retina and LGN.

Thus, the GNLM modeling methods reveal how non-linear computation performed is performed the RG synapse, and allows for more general characterization of non-linear computation throughout the visual pathway.

Authors’ Affiliations

Department of Biology, University of Maryland
Department of Mathematics, Cooper Union School of Engineering
Department of Neuroscience, Mt. Sinai School of Medicine


  1. Butts DA, Weng C, Jin JZ, Yeh CI, Lesica NA, Alonso JM, Stanley GB: Temporal precision in the neural code and the time scales of natural vision. Nature. 2007, 449: 92-95. 10.1038/nature06105.PubMedView ArticleGoogle Scholar
  2. Butts DA, Jin JZ, Weng C, Alonso JM, Stanley GB: The computation underlying the precise timing of visual neuron spike trains. [http://biology.umd.edu/ntlab/GNLM_LGN.pdf]
  3. Paninski L: Maximum likelihood estimation of cascade point-process neural encoding models. Network: Comput Neural Syst. 2004, 15: 243-262. 10.1088/0954-898X/15/4/002.View ArticleGoogle Scholar
  4. Ahrens MB, Paninski L, Sahani M: Inferring input nonlinearities in neural encoding models. Network: Comput Neural Syst. 2008, 19: 35-67. 10.1080/09548980701813936.View ArticleGoogle Scholar
  5. Carandini M, Horton JC, Sincich LC: Thalamic filtering of retinal spike trains by postsynaptic summation. Journal of Vision. 2007, 7: 1-11. 10.1167/7.14.20.PubMedView ArticleGoogle Scholar
  6. Wang X, Wei Y, Vaingankar V, Wang Q, Koepsell K, Sommer FT, Hirsch JA: Feedforward excitation and inhibition evoke dual modes of firing in the Cat's visual thalamus during naturalistic viewing. Neuron. 2006, 55: 465-478. 10.1016/j.neuron.2007.06.039.View ArticleGoogle Scholar


© Butts and Casti; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.