In vivo recording of postsynaptic potentials and low threshold spikes in W cells of the cat's lateral geniculate nucleus

1990 ◽  
Vol 81 (2) ◽  
Author(s):  
F.-S. Lo ◽  
S.M. Sherman
Keyword(s):  
Lateral Geniculate Nucleus ◽  
Postsynaptic Potentials ◽  
Lateral Geniculate ◽  
Low Threshold

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

1995 ◽  
Vol 74 (3) ◽  
pp. 1222-1243 ◽  
Author(s):  
P. Mukherjee ◽  
E. Kaplan
Keyword(s):  
Computational Modeling ◽  
Spike Train ◽  
Lateral Geniculate Nucleus ◽  
Transfer Functions ◽  
Temporal Frequency ◽  
Relay Cell ◽  
Lateral Geniculate ◽  
Low Pass ◽  
Band Pass

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.(ABSTRACT TRUNCATED AT 400 WORDS)

Oscillations and Long-Lasting Correlations in a Model of the Lateral Geniculate Nucleus and Visual Cortex

2000 ◽  
Vol 84 (4) ◽  
pp. 1863-1868 ◽  
Author(s):  
Kyle L. Kirkland ◽  
Adam M. Sillito ◽  
Helen E. Jones ◽  
David C. West ◽  
George L. Gerstein
Keyword(s):  
Experimental Data ◽  
Visual Cortex ◽  
Cell Membrane ◽  
Lateral Geniculate Nucleus ◽  
Feedback Inhibition ◽  
Frequency Range ◽  
Lateral Geniculate ◽  
Low Threshold ◽  
Thalamocortical System

We have previously developed a model of the corticogeniculate system to explore cortically induced synchronization of lateral geniculate nucleus (LGN) neurons. Our model was based on the experiments of Sillito et al. Recently Brody discovered that the LGN events found by Sillito et al. correlate over a much longer period of time than expected from the stimulus-driven responses and proposed a cortically induced slow covariation in LGN cell membrane potentials to account for this phenomenon. We have examined the data from our model, and we found, to our surprise, that the model shows the same long-term correlation. The model's behavior was the result of a previously unsuspected oscillatory effect, not a slow covariation. The oscillations were in the same frequency range as the well-known spindle oscillations of the thalamocortical system. In the model, the strength of feedback inhibition from the cortex and the presence of low-threshold calcium channels in LGN cells were important. We also found that by making the oscillations more pronounced, we could get a better fit to the experimental data.

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