Models that contain the Cell : Locust Lobula Giant Movement Detector (LGMD) neuron

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    Models   Description
1.  Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)
This will reproduce Figure 9 of Gabbiani and Krapp (2006) J Neurophysiol 96:2951-2962. The figure simply shows that a leaky-integrate-and-fire model cannot reproduce spike frequency adaptation as it is seen experimentally in the LGMD neuron.
2.  LGMD - ON excitation to dendritic field C
Neuron model code used in "Contrast-polarity specific mapping improves efficiency of neuronal computation for collision detection". This model adapts previous LGMD model to investigate the effects of newly discovered ON excitation impinging on dendritic field C
3.  LGMD impedance (Dewell & Gabbiani 2019)
"How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing . Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration."
4.  LGMD Variability and logarithmic compression in dendrites (Jones and Gabbiani, 2012, 2012B)
A compartmental model of the LGMD with a simplified, rake shaped, excitatory dendrite. It receives spontaneous input and excitatory and inhibitory synaptic inputs triggered by visual stimuli. It generates realistic responses to looming through the velocity dependent scaling and delay of individual excitatory synaptic inputs, with variability. We use the model to show that the key determinants of output variability are spontaneous input and temporal jitter of the excitatory inputs, rather than variability in magnitude of individual inputs (2012B, J Neurophysiol). We also use the model to analyze the transformation of the excitatory signals through the visual pathway; concluding that the representation of stimulus velocity is transformed from an expansive relationship at the level of the LGMD inputs to a logarithmic one at the level of its membrane potential (2012, J Neurosci).
5.  LGMD with 3D morphology and active dendrites (Dewell & Gabbiani 2018)
This is a model of the locust LGMD looming sensitive neuron from Dewell & Gabbiani 2018. The morphology was constructed based on 2-photon imaging, and active conductances throughout the neuron were based on sharp electrode recordings in vivo.
6.  Spike frequency adaptation in the LGMD (Peron and Gabbiani 2009)
This model is used in the referenced paper to demonstrate that a model of an SK-like calcium-sensitive potassium (KCa) conductance can replicate the spike frequency adaptation (SFA) of the locust lobula giant movement detector (LGMD) neuron. The model simulates current injection experiments with and without KCa block in the LGMD, as well as visual stimulation experiments with and without KCa block.

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