The maintained discharge of neurons in the cat lateral geniculate nucleus: Spectral analysis and computational modeling

Pratik Mukherjee, Ehud Kaplan

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The maintained discharge of neurons along the early visual pathway in mammals constitutes the 'noise' from which the visual signal must be discriminated. The statistics of this background noise in cat retinal ganglion cells (RGCs) have been shown to conform to that of a gamma-distributed renewal process (Kuffler et al. 1957; Barlow and Levick, 1969), and power spectrum analysis reveals that this property allows for low noise levels at the temporal-frequency range (0-10 Hz) most important for visual performance (Troy and Robson, 1992). In this study, we compare the statistics of the maintained discharge of cat lateral geniculate neurons with those of its RGC input by simultaneous recordings of spikes and S-potentials in single relay cells of the cat lateral geniculate nucleus (LGN). We demonstrate that, during primarily tonic spiking activity, the LGN maintained discharge preserves the renewal process statistics of its RGC input and also generates relatively little noise at the temporal frequencies important for vision. However, during burst spiking activity, the renewal process model breaks down and increased noise is generated at 2-10 Hz. This suggests that optimization of the visual signal/noise ratio is not a prime consideration in the behavioral states associated with bursting activity in the LGN. The occurrence of burst spikes in LGN relay cells is dependent on the activity of T-type calcium channels in their plasma membranes (Jahnsen and Llinas, 1984a,b). We show that a computational model of LGN relay cells that incorporates T-channel kinetics (Mukherjee and Kaplan, 1995) can correctly simulate LGN maintained discharge statistics during both tonic and bursty firing conditions, and indicates an essential role for this ion channel in determining the dynamic noise properties of the LGN. We also use the computational model to predict how the burstiness of the LGN maintained discharge is affected by the statistics of its RGC input.

Original languageEnglish
Pages (from-to)529-539
Number of pages11
JournalVisual Neuroscience
Volume15
Issue number3
DOIs
StatePublished - May 1998

Keywords

  • Computational neuroscience
  • Lateral geniculate nucleus
  • Maintained discharge
  • Noise
  • Vision

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