title: A simple model of retina-LGN transmission.
abstract: 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.
authors.affiliation: Fishburg Department of Neuroscience, Mount Sinai School of Medicine, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA. alex.casti@mssm.edu
authors.forename: Alexander
authors.initials: A
authors.lastname: Casti
authors.forename: Fernand
authors.initials: F
authors.lastname: Hayot
authors.forename: Youping
authors.initials: Y
authors.lastname: Xiao
authors.forename: Ehud
authors.initials: E
authors.lastname: Kaplan
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D005829:Geniculate Bodies / Q000502:physiology*; D008959:Models, Neurological; D009433:Neural Inhibition; D010775:Photic Stimulation / Q000379:methods; D011237:Predictive Value of Tests; D012160:Retina / Q000502:physiology*; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology