Dynamics of neurons in the cat lateral geniculate nucleus: In vivo electrophysiology and computational modeling

P. Mukherjee, E. Kaplan

Research output: Contribution to journalArticlepeer-review

107 Scopus citations


1. We investigated the time domain transformation that thalamocortical relay cells of the cat lateral geniculate nucleus (LGN) perform on their retinal input, and used computational modeling to explore the biophysical properties that determine the dynamics of the LGN relay cells in vivo. 2. We recorded simultaneously the input (S potentials) and output (action potentials) of 50 cat LGN relay cells stimulated by drifting sinusoidal gratings of varying temporal frequency. The temporal modulation transfer functions (TMTFs) of the neurons were derived from these data. The burstiness of the LGN spike trains was also assessed using objective criteria. 3. We found that the form of the TMTF was quite variable among cells, ranging from low-pass to strongly band-pass. The optimal temporal frequency of band-pass neurons was between 2 and 8 Hz. In addition, the TMTF of some cells was nonstationary: their temporal tuning changed with time. 4. The temporal tuning of a cell was directly related to the degree of burstiness of its spike train. Tonically firing relay cells had low-pass TMTFs, whereas the most bursty neurons exhibited the most sharply band-pass transfer functions. This was also true for single cells that altered their temporal tuning: a shift to more band-pass tuning was associated with increased burstiness of the spike train, and vice versa. 5. We constructed a computer simulation of the LGN relay cell. The model was a simplified five-channel version of the thalamocortical neuron model of McCormick and Huguenard. It incorporated the quantitative kinetics of the Ca2+ T channel, as well as the Hodgkin-Huxley Na+ and K+ channels, as the only active membrane currents. To simulate the in vivo dynamics of the relay cell, the input to the model consisted of trains of synaptic potentials, recorded as S potentials in our physiological experiments. 6. When the resting membrane potential of the model neuron was relatively depolarized, the model's TMTF was low pass, with no bursting evident in the simulated spike train. At hyperpolarized resting membrane potentials, however, the modeled TMTF was band pass, with frequent burst discharges. Thus the biophysical model reproduced not only the range of dynamics seen in real LGN relay cells, but also the dependence of the overall dynamics on the burstiness of the spike train. However, neither of these phenomena could be simulated without the T channel. Thus the simulations demonstrated that the T-type Ca2+ channel was necessary and sufficient to explain the LGN dynamics observed in physiological experiments. 7. We conclude that the temporal properties of LGN relay cells are dynamic, not static. Our data do not support the view of the LGN as having two distinct states (bursting or tonic). Instead, they show that the tuning of LGN neurons varies continuously with the level of bursting in the spike train. The results from our computational modeling indicate that bursting and tuning are both governed by the resting membrane potential of the cell, through its influence on the activity of the T-type Ca2+ channel. 8. Neuromodulatory influences on the LGN, which originate from visual cortex and from brain stem arousal centers, can regulate the resting membrane potential of relay cells. In this way, the LGN can act as a 'tunable temporal filter' of visual information, screening out steady-state activity during drowsy or inattentive states yet still allowing for the faithful transmission of the retinal input during awake, alert behavior.

Original languageEnglish
Pages (from-to)1222-1243
Number of pages22
JournalJournal of Neurophysiology
Issue number3
StatePublished - 1995
Externally publishedYes


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