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K L Kirkland

Publications and source records attributed to K L Kirkland.

5 recordsLinked to original sources

Oscillations and long-lasting correlations in a model of the lateral geniculate nucleus and visual cortex.

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.

Computer Simulation↗

A model of cortically induced synchronization in the lateral geniculate nucleus of the cat: a role for low-threshold calcium channels.

Recently Sillito et al. (Nature 1994;369:479-82) discovered correlations in the spike trains of a relatively distant pair of cat lateral geniculate nucleus cells when simultaneously stimulated by a drifting grating; no such correlation occurs when the visual cortex is removed. In a further analysis of the data, we have found that short, high-frequency bursts contribute substantially to the synchronization and we hypothesize that the origin of the bursts is the low-threshold calcium spike. Guided by this hypothesis, our model of the corticogeniculate pathway and early visual system reproduces the experimental data in nearly every detail, as well as making predictions about cortical activity during the synchronizing process. We also discuss the possible behavioral relevance of correlations in the geniculo-cortical loop as well as other neural systems.

Action Potentials↗

Neural assemblies: technical issues, analysis, and modeling.

Neurons often work together to compute and process information, and neural assemblies arise from synaptic interactions and neural circuits. One way to study neural assemblies is to simultaneously record from several or many neurons and study the statistical relations among their spike trains. From this analysis researchers can try to understand the nature of the assemblies, which can also lead to attempts at modeling the underlying mechanisms. In this review we discuss three important parts of this process: (1) technical issues related to simultaneously recording more than one single unit, (2) ways of analyzing the data and (3) recent models offering hypothetical mechanisms of neural assemblies, especially models which incorporate feedback.

Action Potentials↗

A feedback model of attention and context dependence in visual cortical networks.

We have modeled biologically realistic neural networks that may be involved in contextual modulation of stimulus responses, as reported in the neurophysiological experiments of Motter (1994a, 1994b) (Journal of Neuroscience, 14:2179-2189 and 2190-2199). The networks of our model are structured hierarchically with feedforward, feedback, and lateral connections, totaling several thousand cells and about 300,000 synapses. The contextual modulation, arising from attention cues, is explicitly modeled as a feedback signal coming from the highest-order cortical network. The feedback signal arises from mutually inhibitory neurons with different stimulus preferences. Although our model is probably the simplest one consistent with available anatomical and physiological evidence and ignores the complexities that may exist in high-level cortical networks such as the prefrontal cortex, it reproduces the experimental results quite well and offers some guidance for future experiments. We also report the unexpected observation of 40 Hz oscillations in the model.

Action Potentials↗